a very good morning all. Today I will be dealing about research designs or epidemiological methods. So research is known as epidemiology when it occurs among a group of people. So epidemiology we can split the word epidemiology as epi, demos and ology. So epi means it is distribution means people you know demography and ology is study. So the study of distribution of something in a group of people is epidemiology. The research is we are searching something again and again. So commonly there are few methods to conduct a proper epidemiological study. So when we come across a problem which occurs among a large group of people we need to do a epidemiological study. So there is fundamental difference between an epidemiologist and a clinician. Clinician is people go to a clinician for getting a checkup or getting a treatment for a particular disease that is an individual. An individual going to a doctor for get a treatment. But what happens when many people are affected with the same disease just like we are seeing the corona or COVID-19 disease. So doctors cannot actually solve the problem. So what happens is epidemiologists go to the public and treat the disease. So what they do is they conduct a study and they find out how it distributes and where it is getting distributed and what is the time place and person distribution and they solve the problem. That is all about epidemiological method. So when to conduct epidemiology and how to conduct epidemiology. So it depends on the disease. So basically we know prevalence of anemia. We have seen leptospirosis. We have seen low birth weight, there is factors and a conventional suture or stapler which is used for suturing the wounds. So all these are different problems and all these four are to be dealt with different epidemiological studies or epidemiological study designs. So basically a research design or epidemiological study research and epidemiology is almost the same. The research is we are trying to find out a new solution or a solution for a problem. Epidemiology is the problem occurs among the people. So basically the designs are divided into the observational and experimental. It is very clear that one is just an observation and one is an experiment. Okay. So when a clinician is treating a particular patient and epidemiologist treating a group of people or a mass of people. Clinician treats the patient within the clinic or within the hospital whereas epidemiologists go to the public and treat the disease. You cannot treat a single patient. You have to find out how the disease spreads, where it is getting started. So all these you have to find out and treat the disease then only the disease will be cured from a mass of people. So observational is an epidemiologist just observing the situation and finding not a solution. It is just like our coronavirus. COVID-19 disease we are not treating the disease by applying any medicine or a vaccination. We are observing where it is getting spread and how fast it is getting spread. So we are asking people to go for a lockdown. So there are a lot of things involved in observation study but the epidemiologist will not do any purposeful intervention from his side. So it will be just an observer. But whereas in experimental the clinician or researcher or epidemiologist will do a intervention from his spot to find out the cause of the disease or find out the effect of a particular drug. So clinician or researcher or epidemiologist will have a significant role in experimental study whereas in observation study he just act as an observer but still find out the solution of the problem. So under observational studies we have the major divisions. One is descriptive study and one is analytical study. So in observation study before that we need to find out a problem. Suppose you imagine a cholera outbreak in your city. So cholera outbreak must have started from a sewage containing water supply and lot of people are getting affected.  You are finding out the people their symptoms and you are trying to find out where it is started and how it is getting distributed which all groups of people is getting affected.  Hypothesis means an assumption your doubt without any evidence without any proper evidence. The data which you collected by asking questions how when where you ask many questions related to the cholera what are the symptoms where it started how it started who all are affected. All you ask and make a hypothesis make an assumption and you could say that a particular restaurant or particular pump or particular common tap is the is you cannot say is it could be it should be an assumption could be the source of infection and this many people are affected. So drinking water from this pipe or drinking water from that particular hotel could be the course of cholera. So you are making a hypothesis that is a descriptive study basic it's a basic thing the first study should be descriptive study in any epidemiological design. So you have a data that is the cholera is caused due to drinking water from that particular pipe a common pipe. But you cannot say that the cholera is due to that disease to say that you need to test the hypothesis which was made in descriptive study. So testing hypothesis you have to do analytical study. So from the data you need to compare the data. So when you do analysis on the data with a comparison group that is known as analytical study. So only after analytical study you can say that this could be the reason for that particular disease. So these are the basic observations very in both case the pharmacist is observing it is not putting any extra intervention from this part it's just observing one is making a hypothesis and other one is testing the hypothesis. Okay so we were seeing the observational study designs that is descriptive and analytical. So the analytical we are testing the hypothesis and descriptive study will be checking or will be creating the hypothesis. So we will be seeing some of the descriptive study design. So you can see an article a study of acute vision loss in postpartum period and HIV associated meningitis people. So it is something like description of a certain condition. So it is known as a case report which is just a descriptive study design. One of the descriptive study design. The next one is pneumocytus, carinineumonia and mucosal candidiasis in homosexual men. So it has become case it is case it is nothing but a compilation of many case reports. These all are descriptive patterns of a study. They are just describing a certain condition in a few people or a group of people. The next one is the prevalence and determinants of hypertension in a group of people. This has become cross sectional study because the people will be asked questions only at one point of time. There will not be any follow-up. Still they are asking only questions only the reports will be checked and data will be collected. Okay so these are and one more thing which I left out is longitudinal study. So the same cross sectional study is being repeated or if there is a follow-up the same group of people is asked questions after a period of time that will become longitudinal study or follow-up study. So in all these studies that is case report, case series, cross sectional study, longitudinal study. We are just describing condition of people and we are trying to create a hypothesis. We are not trying to analyze anything out of the data but we are just seeing the data and we are creating hypothesis. But whereas in analytical study which is also part of observation study, we can see that there is one study design which is known as case control study. We will be dealing in detail about all these study designs, case control and cohort study. But as of now case control study is nothing but an analytical study. So in all analytical study that is in case control and cohort, we are trying to test the hypothesis. So there will be always a comparison group. Then only testing of hypothesis is possible. So in this case that is control group will be there. So this is the comparison group. So risk factors of development of color total carcinoma. So these are the patients and we will be taking one group of people without the disease that is without the color total carcinoma and we will be comparing the risk factors and we will try to find out or we try to test the hypothesis and reach into a conclusion. So this is case control study. This is always a retrospective study. We are going back. We will be asking questions or we will be taking data from the previous records. But this is one of the famous study the Framingham Heart Study. This is a cohort study. This is a forward looking study. This is the study goes to the future. Okay, this study started dated back to 1948 and the study is still going on. So this is known as cohort study. Cohort means a group of people pushing common characteristics. So people with cardiovascular disease there risk factors will be assessed in the future or their risk factors will be assessed at frequent interval of time. We will not be asking questions and we will be taking data from the previous history. Instead we will be checking data, we will be collecting data from the follow-ups. Once the study started we will keep follow-up at regular interval and we will be collecting data. So such study is known as cohort study and in cohort study also there will be a comparison group. People who non-developing this cardiovascular disease at the beginning of the study cohort study there will not be any disease in the cohort group. Over the period of time few will develop the disease and the other will remain as a comparison group. It is little complicated. We will deal this later but right now we will just check the concept cohort means a group and at the beginning of the study there will not be any disease in the group and over a period of time cohort will be divided into two that is disease and non-disease and we have a comparison group that is non-disease and we will be comparing this and we will be comparing the risk factors. Whereas in case control study the study is started with case. Already cases are there we just need to find out some controls age and gender match controls and we will be comparing and we will be checking the risk and we will be testing the hypothesis. So the analytical study has basically two parts that is two components one is case control study and cohort study. Case control study means we will be doing on the cases and we will be taking controls. Cohort study we are doing on a group of people. Those people have not developed a particular disease yet they might develop the disease suspected disease in future. So cohort study is always very time consuming very expensive very administratively complicated whereas case control study is very simple you can conduct it very easily because case is already there we just need to find out the control and conduct study whereas in cohort study sometimes the cohort will not turn into two groups or a particular period of time so it might take years to complete a cohort study but always cohort study is better than case control study. So our study designs have become like this observational and experimented and descriptive study and analytical study descriptive has four parts our four designs case report series of case report that is case series cross sectional study population will be taken only at one point of time longitudinal study the same group of people will be followed up to regular intervals or a future period in analytical study where the hypothesis will be testing there are two components case control and cohort study case control is retrospective cohort is prospective study in case control cases are already there we need to find out the controls age and gender matched controls in cohort study the cohort group will not be having any disease at the beginning of the study over a period of time it might develop into a disease time non-disease in both the study designs we need to have a comparison group in case control study the control group is our comparison and cohort study there will be a non-diseased group for comparison. So finally we will be testing the hypothesis and we will be doing a risk estimation so all these risk estimation will be dealing in future classes so the experimental study design the in the beginning i have told you the basic two designs are observational and experimental so far we have completed observation study in observation study descriptive and analytical where the researcher or epidemiologist just observing the condition or the disease whereas an experimental study design the clinician or researcher has a significant role or he will do an intervention purposefully on the people or the study subjects so you can see that a study a comparison between test flow rate and proper fold in improving the laparoscopic sleeve gastritomy in this two group of people are getting two different drugs okay so all these drug trials will be experimental study because two group of people the researcher are giving two different group of medicine to two different group and finding out their outcome and comparing this and trying to find out which drug is better in observation study there will not be any such thing any intervention any manipulation from the part of researcher put in experimental study there will be manipulation or intervention from the part of our research that is two different drug will be given to different groups and will be checking the outcome of the disease outcome of the medicine on the disease so this is known as randomized control trial the important part is trial trial means the investigator has a significant role so control means there will be always a controlled group in this study randomized means the groups will be randomly selected the participants will not be knowing which group they are going to participate there will be randomly selected to or allocated to the groups so that is about randomized control trial so in experimental study we have two designs randomized control trial and non-pantomized trials in non non-randomized only the differences the participants are not allocated randomly they will be knowing which group they are being being by which group they are getting allocated to or getting participated so always randomized trial has a better advantage than the non-randomized okay so all this study designs will be dealing in detail in later classes so this is all about the epidemiological study design this is a very broad classification the first division is observational and experimental and observational while the clinician has just an observable descriptive and analytical this is testing hypothesis sorry this is creating hypothesis and descriptive and analytical this is testing hypothesis and descriptive we have case report case series constitutional longitudinal study designs in analytical we have case control and cohort studies whereas in experimental study we have randomized trial and non-randomized trial this is a very broad classification but this is apt for to to understand the basics of a epidemiological studies so we'll be dealing in detail about the study within in future classes thank you good morning everyone let's continue our epidemiology session so today we'll be seeing the descriptive epidemiology so I was talking about epidemiological basic study designs that is observational and experimental so in observational we had descriptive and analytical epidemiology so today we'll be seeing in detail about descriptive epidemiology so descriptive epidemiology we had seen many study design one was case report case series longitudinal study and cross-sectional studies so how to conduct a descriptive epidemiological study so this is a very common study which everyone can easily perform and it is a very first step of any type of study this is just a collecting data collecting data of any group of people regarding any particular disease or anything so that is all about descriptive epidemiology in short so we can say that this is a first phase of epidemiological study so we will be observing the distribution of disease how it is distributed among the people and what are the characteristics of the disease associated so these are the common things which we will be doing in descriptive epidemiology so all these things you can see the example of a descriptive data this is number of death per person and by one lakh people one lakh a life birth so you can see that the death is going on decreasing as time goes forward so this is 1936 and this is 1975 so the number of death is decreasing over a period of this is sorry not 1975 this is 1750 to 1975 so this is the data of the child death in among one lakh life births so this is an example of descriptive data similarly the death rates of heart disease in six countries okay so these are the death rates which is showing in these dotted line graphs so these are the death rate in the y-axis and the year in x-axis so it is just giving a data of death rate in various countries during this period 1952 2010 so what are the basic steps in descriptive epidemiology these are the fundamental steps so the first is defining the population so suppose we have a problem in front so we are going to conduct a study in a population so we need to define the population okay so the second one is defining the disease under study and the third one is describing the disease by using time person and place the fourth step is measuring the disease and the fifth step is comparing with non-antices and finally the ultimate aim of descriptive study is to formulate hypothesis so we'll come in detail by each step the first one is defining the population so we go to a population which includes lot of people various gender various socioeconomic status various occupation various classes so we need to define our population to be studied suppose the same example we took we take x-ray we're taken an example of cholera so we need to constrict our population to a group of people who might have had consumed water the suspected water because water consumption might have ghost this cholera disease so in a particular period of time people who have had consumed water from a particular hotel or a particular common tap must be our population we cannot just take the entire population because the population will be very huge so we need to constrict and we need to clearly define our population where we are going to conduct our study so that is our population defining otherwise the the study which we are planning to conduct will be very difficult because the population will be too huge it will take up majority of our time expenditure and we'll end up with nothing so if not to get a good result we need to define our population exactly which group of people we want to conduct the study so you can take the example as cholera disease people who have consumed water suspected water from the particular source should be your population so the next is say suppose you can take an example example the same example you can take this this could be the total population in that town or society wherever it is so the target population will be too huge okay so you cannot include all the people in that population you have to take a small sample of the population so when we define our population the population will be little more little more big still it will be as big as the study is not possible so we need to take a sample from the population so this will be our target population people who have had consumed water from the suspected source so we may not be able to take entire the target population target population is the total population who might have had drank that particular water from that particular source there will be many people there will be thousands of people so we cannot take the entire population so what we do is we take a sample out of it it should be representative so the second step is the first step was defining the population the second step is defining the disease under study okay so population already defined so next thing is to define the disease under study so the cholera disease or any disease which need to be clearly defined so what happens is we need to keep an operational definition okay not the exact definition we need to keep an operational definition can give you an example tonsillitis is a disease okay so our operational definition will be see the normal definition is like inflammation of the tonsils caused by infection usually with streptococcus pyrogens but we'll convert it into the presence of large red tonsils with white x-ray which on trot sap culture that has predominantly streptococcus pyrogens so our operation definition is this so we will be considering patients which which follows or which actually representing the disease operation definition is like presence of enlarged red tonsils with a red white x-ray which on trot sap which has streptococcus pyrogens this is our operation definition so our cases will be only persons which follows or which actually have this operation definition rather than our this definition so this is a WHO definition or a clinical definition so we'll convert the clinical definition into operation definition and we follow the operational definition for all the patients only the patients which has operational definition criteria will be considered as case otherwise they will not be considered as case why we are doing this operation definition is to restrict the people restrict the entry of people otherwise we might get a lot of people which will be very difficult to incorporate into this study because epidemiological study is always limited to a smaller sample because larger studies are not possible because of many factors so we need to control our population size but the same time we need to make sure that the population is representative and the sample should represent of that population so that's why we are defining our population and we are defining our study so that the population size can be restricted without losing the quality of population so the sample will be having the sample will be having the same properties of the population it is supposed to have the same properties of population so those two steps of one is the defining our population and the second one is defining the disease so operation definition we keep in cholera it will be a different operation definition i just kept this translate is for easy understanding so once we defined our study it is easy to categorize the patients as deceased or non-deceased if you keep clinical definition we make it a lot of patients some of them might not be useful for our objective or our particular study so operation definition is always important in any epidemiological study that is descriptive study the next part is the most crucial part of our descriptive study that is the describing the disease we need to describe the disease under three headings that is time place that is time place and person so we need to describe the disease so if we say it is time we commonly ask these three questions that is time distribution when is the disease occurring so if we go to the cholera example we ask the people when was it happened when is it occurring and where is it occurring and who is getting the disease these three things we need to find out the time place and persons when where and who these three are the most important thing in descriptive study so time distribution we may go into detail so we need to find out how the usually diseases are getting getting classified under short-term fluctuation period fluctuation long-term or secular trends p the diseases are very different in its output how they present how they show symptoms in people are very different so patterns of diseases are different so time distribution we know short-term fluctuation if it is epidemic it will come suddenly a lot of a lot of cases appear overnight in a two three days of period and it will go just like that so that is epidemic occurrence of occurrence of and more number of cases that is excess number of cases that is epidemic but this is very short-term fluctuation we know common cold influenza short-term fluctuation which is having a very shorter duration so types of everyone will go through a little bit about the short-term fluctuation of the epidemics that is common source epidemic and the propagated epidemic we know person to person the corona is spreading from person to person from other quote from annual reservoir if it is based on the source point source very single person is spreading the disease or continuous or multiple exposure or slow epidemic this is just a sub part of this class that is types of epidemics so under short-term fluctuation or under time distribution we are seeing short-term fluctuation and in short-term fluctuation it is commonly epidemic and we are seeing just examples of some of the epidemics epidemics can be classified as common source propagated and slow epidemics okay we will not go into much detail point source means a single person is giving a lot of disease or a point source is giving a lot of cases that is Bhopal case tragedy or a foot poisoning common source common sources will of contaminated water or a prostitute infected with gonorrhea so this is a common source which spreads repeated exposure but before I was talking about single exposure so single exposure is a single place or a single locality which is spreading a lot of diseases not a single person so it is like foot poisoning and Bhopal case tragedy and here it comes a common source that there we have a prostitute infected with gonorrhea or a well of contaminated water where a single source or a common source is spreading diseases and propagated epidemics with seasonal trends and cyclic trends so seasonal trend is we know measles usually occurs in early spring and respiratory diseases which we commonly see in winter and GIT which commonly seen in summer this is seasonal trend okay whereas in cyclic trend we know before the vaccination era measles used to happen every two to three years Robola every six to nine years and influenza every seven to ten years after the vaccination period this all are not very common so automobile accidents are more frequent on weekends that is Saturdays and Sundays it is a cyclic trend before we study seasonal trend that is upper respiratory tract which is common in winter and measles in early spring and long term or secular trend is like common heart disease or lung cancer diabetes which is very common in developed countries but now our India and other developing countries are also becoming cases are increasing but usually it happens over a very long period so secular trends or long term trends it is like commonly seen this type of diseases in the western countries and now it is slowly slowly changing and a big shift will happen in a very longer period of time so what we have seen is epidemics okay the type of epidemics it was under time distribution short-term fluctuation and epidemics the types of epidemics like common source propagated and slow epidemic and the common source the bubble gets tragedy and food poisoning and continuous or repeated exposure that is prostitute of gonorrhea and well of contaminated water and propagated epidemics and periodic fluctuation okay so before we were seeing short-term fluctuation now we are seeing periodic fluctuation just a little bit messy it comes in between the descriptive epidemiology but anyway epidemic is a commonly asked question so you need to know in detail about epidemic so seasonal trends periodic fluctuation we have seasonal trend and cyclic trend seasonal trend we talked about missiles and respiratory diseases cyclic trends pre-vaccination and vaccination era and automobile accidents long-term trends the cancer we've seen in the developed countries so we are covered the time distribution now the place okay so place we need to find out where all it is happened that this is how it is distributed in a geographical area so what we need to find out if it is a big disease or if it is a pandemic or a disease which happened in a town state or a district or a country we need to find out the local distribution the urban rural differences national and international variations okay so international variations we were saying that cancer is very common in cancer of stomach which is common in Japan not in US so the oral cancers are common in India compared to western countries so these are a place distribution international variation so national variations we know some areas are endemic diseases which are just like fluorosis goiter malaria nutrition deficiency which are very prevalent in certain parts of our country this is not very prevalent all over the country but it is some parts of the country malaria fluorosis endemic fluorosis here in Kerala which is in alopea and palakkad so rural urban variations certain diseases are very common in rural sector like abdominal disease and certain diseases like dental care is common in urban setup and the disease like bronchitis accidents lung cancer cardiovascular which is common in urban than the rural but whereas the zoonotic and skin diseases which are common in rural setup so there will be variations for the disease according to the place rural urban the national variation international variations and local distributions local distribution how the disease is locally distributed so this is one of the famous example of the epidemiology has come into existence it is done by john snow is known as father of epidemiology so this was a case in cholera so this pump a was spreading the disease so after he did a sport map he found out that the many casualties are around the pump a so in local distribution you can do a sport map and sport the casualties or the cases happened so you'll get to know that it might be concentrated around some point that might be the point which caused the disease so in this case this was a london happened in 1840s in london broad street church a pump was here which is just contaminated broad street which was contaminated with sewage supply and it was producing lot of cholera patients so he accidentally found on that the pump was the reason and later the pump was changed and the disease abruptly stopped so this was an invention or this was a discovery by the great epidemiologist john snow and that's why it's known as father of epidemiology so this is a sport map this looks like a town map where these black dots are the casualties due to that particular disease so if we do a sport map in a very small area we'll get to know the we'll get to know a better picture of the disease so this was about place so we have seen time and place the next one is person distribution so how widely it is distributed within different accordingly like age sex occupation marital status habits and social class so we collect data from persons and we categorize into age gender occupation marital status so we can easily understand in which age group in which gender which social class which marital class it is distributed so usually missiles happen in childhood cancer in middle age and atherosclerosis in old age so these are the few diseases which is very prevalent in certain age groups and gender few diseases are prevalent in males and few are in females and in occupation related diseases workers in coal mines usually have selicosis these all are examples one we do a study we need to find out where it is actually prevalent these are examples which i was talking about and social class few diseases are very common in upper class few are very common in rural class i mean not rural areas that is our low socioeconomic background we have reached the four steps so first was the defining the population then defining the case that was describing the disease space on time place and person in time we had fluctuation short term periodic fluctuation and long term trends and we have seen epidemics and time place and person so the fourth step is measuring the disease so we need to measure the disease by using any of our tools so tools are like the measurement of epidemiology will be done by epidemiological tools that are rate ratio rate ratio and proportion so we need to understand the amount of disease we need to quantify the amount of disease by using rate ratio and proportion so the tools of epidemiology i'll be explaining another class so we need to measure the disease based on these tools of epidemiology so usually we calculate the incidence and prevalence incidence is the new cases and prevalence is the total cases so prevalence means how much percentage of the people are affected by that disease and incidence is how fast it is spreading so in epidemic we need to find out the incidence in a chronic diseases like cancer heart disease we need to find out the prevalence so incidence is very vital in controlling an epidemic so usually incidence can be found out in longitudinal studies prevalence can be found out in cross-section study the fifth step is comparing with non-entersis once you get the data we need to compare with other population where the same problem has happened we need to compare with other population and subgroups of the same population so ultimately we get an idea of the disease etiology okay once we get the idea of disease etiology by after comparing this we need to do the hypothesis this is the last step of descriptive epidemiology formulation of hypothesis hypothesis is nothing but an assumption after arrived from the observation after arriving from the collected data so once you collected data you arrive at a formulation of hypothesis so hypothesis is assumption about the particular problem so drinking water from that particular pipe or drinking water from that particular restaurant could be the course of cholera so we cannot say that it is a course you should say it could be the course and in the next study design that is an analytical study we are going to test the hypothesis whether it is true or false and we reject the hypothesis that is the second step so we have come to the steps okay so I'll just recap it the first step was defining the population defining the population we have defined population and the disease operational definition we are given and describing the disease based on time place person and measuring the disease by using in tools of epidemiology incidents and problems comparing with other groups of same problem and formulation of hypothesis so a little bit tricky part is the operation definition we need to change the clinical definition to operational definition I will come into the third step you have time place in person in time we will be studying more about epidemics that is comes under short term fluctuation so epidemic is a sudden increase in number so common source propagated epidemic and slow epidemic common source has again a different division that is single exposure and continuous exposure single exposure is vocal gas tragedy continuous exposure is well of contaminated water and propagated epidemics which is person to person transmission and the periodic fluctuation we have seasonal trends and cyclic trends and long term of cyclic trends is another thing the second part of the third step is place distribution describing the disease and the place so there will be various variation international national rural urban and local and the third part of third step that is describing the disease under person based on age sex occupation manager status and etc so the fourth step is measuring the disease based on the tools of epidemiology commonly we use incidence and prevalence at the fifth step is comparing with non-indexes we should compare it with different population and subgroups and finally we arrive at a hypothesis that is a proposition or a supposition or an assumption about the cause and outcome so this could be the cause for this disease this could be the that cholera could be the due to drinking water from that particular pipe or from a restaurant that reserve hypothesis so yesterday we had seen types of epidemiological studies just to recap case report case series cross-sectional and longitudinal studies so case reports just explaining a case whereas case series it's like a compilation of case report which happened at different time and different place in the same problem will be repeatedly mentioned from different parts of the world so that is case series in cross-sectional study the population will be taken only at one point of time so this is a cross-sectional study relationship of stress and dental caries among the students in bank lucidity so these students will be asked about stress and their dental caries only one point of time okay so longitudinal studies the same population will be checked at frequent interval of time or there will be a follow-up for an example the health complaints after a malortress chemical explosion a longitudinal study here you can see that the survey for 18 months which was started in 2008 and 2012 so in cross-sectional studies it was just like a study will be done and study will be done either in 2008 or 2012 there will not be any follow-up in cross-sectional study again you can see that association between periodontal condition and microbiota and women during pregnancy longitudinal study so here also there will be definitely a follow-up so in cross-sectional study sample will be surveyed only at one point of time whereas in longitudinal study t1 t2 the same sample will be time one and time two there will be time three four there will be follow-up in longitudinal study okay so that's all about descriptive study i have explained in detail about descriptive study so the next class will be dealing with the analytical study okay thank you very good morning welcome back to my classes will be continuing a session on epidemiology so today we have a small topic and that is measurement in epidemiology how do you measure measure things in epidemiology so let's see what are the tools of measurement so we had covered epidemiology in detail all the study designs that is descriptive analytical and experimental so once we start the study that is epidemiology how do you measure it so we have a lot of things coming across in epidemiology that is mortality which means death mobilities means like cases disability disease attributes a lot of things so today we will be seeing only the mobility measures that is what really important as an epidemiologist remaining are also important but we will be seeing mobility measures mobility means mostly the cases mortality is the death okay so tools of measurement so tools we know every professional has tool if we go to a doctor he has tool as BP apparatus thermometer so using this he measure our blood pressure and our temperature the way we'll come to a diagnosis so likewise an epidemiologist is also having tools to measure the mortality or mobility whatever it is so the basic tools are proportion rate and ratio okay so the proportion rate and ratio so the proportion is nothing but percentage it is like we are calculating the number of people in a group of people and multiplying it with 100 so there will be a numerator which is a part of denominator because the cases will be from within the population so numerator and denominator are connected and there will be a multiplier of 100 and there will not be any time factor why it is important we'll come to know once we see the rate okay so if it is a yellow circle among the total means one by three here it is two by six so we just multiply it with six hundred so here is an example which I was to show in my lectures so what proportion of this class are multi fans so the multi fans divided by the total class into 100 will get the proportion so similar the Mohanlal fans so the total number of fans divided by total number of students in the class into 100 will give the proportion of that particular thing okay so we'll come to the real-life example what proportion of the population is suffering from diabetes we get the data of diabetes patients and divide from total population will get the proportion of diabetes patients so second one we have seen is rate here the time factor comes okay so the time factor is only comes in rate so we have seen cricket matches so usually we seen over rate run rate so much runs is code per hour how much hours is ball per hour so all these are rate so always there will be a time factor so numerator is part of denominator definitely obviously it will be a part of denominator and there will be a multiplier usually we multiply rate with 1000 and proportion with 100 we can do it with 100,000 it doesn't matter but usually we do it with 10000 it has a time dimension this is the most important thing rate is always expressed in time dimension will get to have a better idea once we see the examples where proportion is just the percentage of people affected with something among the total population there is no time factor is involved so rate is we commonly say death rate over so death rate how do we calculate death rate is number of deaths in one year by meteor population and 2000 so this meteor population we usually take in statistics the meteor population the population which is present on the July 1st that is a mid year because we take six months up to June and six months July to December probably July first will be the meteor population so the population which is present on the meteor day will be taken as media population so here numerator denominator and there will be a time factor okay so the run rate is another example so rate involves time dimension the last one is ratio ratio is like male to female ratio husband to wife ratio doctors to population ratio here the striking feature is numerator and denominator are two different entities in proportion and rate it is expressing the same factor but in ratio the numerator and denominator are two different things males and females doctors and patients husbands and wives students and teachers they are two different entities so numerator is not a part of denominator so suppose sex ratio that is male to female in kerala the male to female ratio is 1000 to 1084 so this male and female are two different entities unlike the rate and proportion so doctor population ratio there is one doctor for every 7500 patients so these two are two different things so numerator is not a part of not a component of denominator there it is two random quantities okay that is rate that is ratio so one teacher five children male to female ratio this is doctor population ratio this is one is to 145 this is percentage that is doctors shortfall at psc level this is 27 percentage of doctor is not present in primary health centers in up 34 percentage is not present that will divide number of people who are supposed to be there and divided by total number who are posted so these are proportion and this is ratio we this to this phc this proportion and rate this is rate okay infant mortality rate this is per thousand live birth for one year so that a period is there time factor is there that one year that is denominator and numerator are part of the same thing here also numerator and denominator part of the same thing but here it is the ratio doctor and population is two different entities that is one is to 495 in goa and kerala it is one is to 811 so this is a summary of tools of eprimony that is ratio proportion and rate okay this is rate it is 11 per thousand live birth in one year okay so here time factor is there in these two cases time factor is not there so summary we have covered three things proportion rate and ratio okay so now let's go to the principles of eprimony so therefore principles basically one is exact observation we need to strict wickedness and accurately precisely take the observation and it should be free from error that means correct interpretation and there should be a scientific reasonable and intelligent explanation and the construction also should be based on knowledge and technical skill okay so i made a acronym every coffee requires sugar that is acrs so exact observation correct interpretation rational explanation and scientific construction okay so this is principles of eprimony the beginning we learned about tools of eprimony mortality i told you we are not going into detail that is death related stuff so we are going directly into the morbidity measures okay so this eprimony is very vast like in ocean so only what we need to learn is based on our objective if it is based on our example purpose or research purpose whatever it is it should be based on our purpose okay so we have morbidity measures basically two morbidity measures are there commonly used one is incidence and one is prevalence so incidence and prevalence we already talked in a case control and cohort study in case control study we get prevalence and in cohort study we get incidence so since cohort study is going in future or forward or prospective looking this is finding new cases okay so which happens in the future time so because cohort study starts without any disease so in future or a period of time they may develop case so that becomes incidence that is occurrence of new cases incidence and existence of new and old case that is prevalence so incidence is always a read so you should mention time factor also for one week this many new cases of one month or one year it depends on the time frame but prevalence is just the proportion of people so if in case control study we just take the number of cases divided by the total population we get the prevalence that includes new and old cases but incidence it is going future so there will not be any old case only new cases will be there prevalence we are checking the background information or previous information until today how many cases are present until today how many cases are present so it includes new and old cases okay so today cases and yesterday's cases or previous month previous year everything comes in prevalence so basically we can say that incidence how many people with the disease are newly diagnosed each year which is like a video this is throughout the year or the follow-up here follow-up study or a prospective study it goes throughout the year whereas the prevalence how many people in a population currently have a disease at present how many of them having disease that is just like a snapshot or a picture okay so that is the basic difference about incidence and prevalence so incidence is a formula number of new cases of a disease in a particular time period divided by total population at risk at the same period into thousand okay so incidence as i mentioned earlier is a rate commonly expressed in thousand multiply with thousand whereas proportion or prevalence we multiply with hundred so incidence is number of new cases and since it is a rate there should be always a time period so just check an example on january 1st 2016 there are 10 people and while reaching on january 31st the patient out of 10 people three people became deceased so it is 3 by 10 over a period of one year so 0.3 per one year so we can calculate it by 3 by 10 that is three cases by 10 over a period of year so this time frame is very important okay so this is we are starting without any disease as i mentioned in the cohort study design okay over one year period only three people got disease so we have to mention the time frame okay incidence which will not be there in prevalence so there are true types of incidence that is incidence rate or incidence density or cumulative density or incidence proportion okay so incidence proportion is it will be like percentage there will not be any time frame but incidence rate is the true incidence or incidence density so you don't get confused with incidence proportion which is coming in percentage okay proportion will be percentage okay so incidence rate is the actual incidence so just an example it's a basic thing cumulative incidence and incidence rate numerator will be definitely cases but the denominator will be different thing in cumulative incidence there they'll take the initial population but incidence rate they take the person time here i'll explain in detail so incidence rate goes from 0 to infinity but whereas cumulative incidence goes from 0 to 1 because it is percentage so maximum value is percentage is 100 so the maximum value will be 1 for cumulative incidence but incidence rate will go to infinity so it is also known as incidence density or proportion probability you don't get confused this is cumulative incidence okay so let's take an example where for 12 people are being followed up for 14 years so the 12 people 12 people are being followed up for 14 years starting from 1980 to 1994 so the first person entered the study at year one and he was followed up for eight years but after that he might have left the study okay so he was under risk okay they all were all were population under risk or to develop a particular disease all were having habit of smoking to we to expect outcome of lung cancer okay so this person was observed for eight years all eight years he was under risk so the time at risk became eight so this is known as eight person here so in in incidence risk or incidence density we calculate person here that is a time frame we calculate in this format that is population under risk okay for the time frame so this has become eight person here the second person entered the study in the beginning and he was followed up for 10 years but in at the air of 10 at the 10th year he developed lung cancer so the person under risk was 10 years okay so once it's developed disease there was no risk it became a disease so this is 10 person here and the third and fourth person it was they entered the study at the beginning and they were followed up for all 14 years and they having that risk but they never developed disease so this became 14 person years each this person died at four year four person year and this person entered the study in 1981 okay then they were followed up for 12 years actually this is 12 not 10 1981 to 1993 it will become 12 okay so that's a mistake and second and the remaining all person entered the study in 1981 so all this time person here we'll calculate and we'll divide from the total number of case okay so the 14 year the three cases have been reported okay the second person fifth person and 11 persons were the cases among all 12 people okay so we are not taking the number of people what we are taking is person year the population under risk the duration of population and risk so this 12 people were under risk for a hundred person years how we get hundred person years is different okay so this is how we calculate incidence risk or incidence density we have to calculate person year so this is how we calculate person year from the beginning of study until they leave or study until they'll develop the study we calculate the duration okay so that's how it is 8 10 10 14 14 hope you're clear about this person year and calculating incidence risk or incidence density so the one more example here the person is getting disease at second year so become only one person here here also one person here because he left the study here he became disease at 1991 so two person here here he left at one second year so one person here so this is three person here this is five six five uh he became deceased here okay this is two actually this is one okay this is also one okay so here there's a slight uh no it's all clear okay so only four people are uh became four people are becoming diseases one two three and four so four cases per person year we have to calculate so this is one one three one three five six five one and one so total 26 person years so four by four cases by 26 person years so four by 26 is 0.15 or 15 by 100 person years or 0.15 person years so this is how we calculate person years so this is incidence risk or incidence density okay so this question mark is the person is lost to follow up as i have mentioned you about the attrition factor in cohort study or follow-up study so he might have left out the study okay and this is uh case so once he become uh case uh after that we won't calculate the risk because the risk was for having the disease the risk was to for developing the disease so he is having only one year for the risk secondary develop the case so the next one we we've seen now is incidence risk or incidence density now it's a cumulative incident that is i have told you this is a percentage okay just an example it's very easy so in 2001 in 2001 there were 5000 572 women aged in 20 to 39 years who were sex workers based on the record of uh whatever 45 were HIV positive during this three-year period or four-year period uh they have it should be 2001 to 2005 so what is the cumulative incidence of HIV positive during this four years okay this percentage again comes in prevalence but the problem is prevalence we don't mention about four years prevalence base we just calculate 45 that is new cases we will calculate the total cases that will come beyond uh 2002 there is no time frame but we can calculate time frame that is uh period prevalence that is different thing but that will include all the new and old case okay so this is cumulative incidence so 45 new cases by total population that is 5572 it will come 0.8 percentage okay so incidence rate uh the denominator you can see person here the time frame is uh present but in cumulative incidence there is no person here it is just percentage it is almost like um prevalence because uh prevalence is a proportion this is a proportion but here we have a time frame okay time frame is important that's why it is different from prevalence so there are a few common example that uh rape number of reported rape cases per one lakh women in 2014 and 2013 so in Delhi we can see this is 1813 uh in 2014 whereas uh 1441 uh in 2013 so this is uh rate okay rate per one lakh women okay so it's a common example so what about incidence it is referring only new cases and it is not influenced by duration of disease that is like uh if a disease happened 10 years back or 5 years back it is coming it's okay we are seeing only new disease but the time frame is different so it is always refers to particular time period and denominator is people at risk we had seen incidence risk how the time person here will be calculated next is the prevalence it is proportion so it is just uh like the old and new cases or a particular period of time there is no time frame it is a time frame is here uh in pre period prevalence but it is checking all the new cases the true prevalence is only one point of time and total population into total population at risk into hundred so there are two types of prevalences one is point prevalence and one is period prevalence so common prevalence is uh as I told you uh 2 by 10 and 5 by 10 20 and 15 percentage so in period prevalence we will be assessing the all number of cases throughout a one year period okay this is not like incidence incidence is different we will be assessing the time person but here we will be taking only the number of cases and total number of population okay so here six people are having disease and 3 4 5 6 into 36 so 6 by 36 that is 0.6 percentage 6 by 6 yes 36 6 by 36 6 by 36 so number of cases are 6 and total population is 6 over the period of one year so it started in jamb first and it ended in december 36 so the percentage is 16 percentage so the period prevalence is 16 percentage over one year period of time okay so the point prevalence will be just on one day or uh period prevalence will be or a period of time it can be one week one month or uh one year or five years let it be any time frame doesn't matter but we will be checking cases or a period of cases and the total population will be taken so the denominator is different in prevalence denominaries will be taken total population but in in incidence uh it is different uh incidence rate will take the time person whereas in cumulative incidence will be taking the uh population under risk okay so here we take all cases some cases might be present before uh 2016 they are carrying over to 2016 these two might be uh became deceased in 2014 if they are cancer patient they might have started the disease in 2010 12 13 nobody knows but they are still being uh cases so we'll count this but in incident cases we'll just see at the beginning of study there's nobody's having disease and over a period of time we'll be checking the incidence of cases okay so in prevalence it is not like that if cases was present even the status of case was present even before the start of the studies will also be counted the in chronic cases chronic disease cases it will always happen because the duration of this is very long in chronic is like hypertension cancer so such cases uh these might be present before this uh checking date so this might be present on cases they became deceased on 2013 14 and still their disease so that also will be counted okay so in incidence that will not be counted because we see in 2016 jam first there should not be any case and we'll follow up for one year two year or five year and we'll count the number of new cases okay and the denominator will be time person here in incidence density okay this is prevalence it will be percentage uh if it is a point prevalence or period prevalence so prevalence is like uh it increases if the duration of disease so i told you like cancer patient it increases if the patient is having a longer natural history and if patient is being case that is if it is a chronic disease the prevalence is also will be increases and if the treatment goes uh prolonged it will increase an increase in incidence when people come from outside it will increase and healthy people if goes from our city to outside the denominator will go less so the prevalence will increase prevalence will decreases all the cases all the uh points against the shorter duration uh better recovery improved curate if decrease incidence immigration of new cases if uh new cases are going out of the city and immigration healthy people are coming into a city all these cases prevalence decreases so prevalence will give you the magnitude of problem okay so and administrative and planning purpose we can use it so this is some common example 40 percentage of uh indian people are underweight population okay so it will become around 30 or 40 crores so how it came on the number of underweight divided by total 130 crore this percentage will get this also like that the 20 million obese women that is 3.7 percentage 9.8 percentage indian men that is 3.7 percentage and 20 million that is 5.3 percentage the denominator will be 130 crore okay so how we calculate the prevalence okay so the so let's take an example which is uh case of incidence risk so this is uh something uh we compare it for prevalence okay so here uh october 1st 2004 to september 30 2005 we are observing this downward arrow means date of onset of disease okay date of death is uh positive sign and upward arrow is recovery if you are taking prevalence uh on april 1st 2005 okay so we have to see how many cases are present on april 1st so one two three four five six and seven okay one two three four five six seven so seven cases are present on april one that is point prevalence okay so let's take total population 100 so our point prevalence on april one will become seven percent so if it is october one one two three four five six okay i haven't uh written it here october one the prevalence uh will be six percentage okay six by hundred here it is seven by hundred but what happened was um one person got uh one person died before april one 2005 and two person two person died but uh before 2005 uh april one what what happened was uh was three person became ill okay so that's why this change here two got out of the study but whereas three came into the study so that is why this became seven seven and this became six okay on september 30 just count one two three four and five because two people died here a new person came here okay so on september 30 this is just five cases one two three four five so five by hundred that is five percentage period prevalence for the period of october one to september 30 okay so how many cases like one two three four five six seven eight nine ten so total ten cases were present we are not bothering when they joined or whether they died or not nothing whether they were there as a case will consider that is a prevalence okay whether they're recovered or they're died uh nothing matters so that is why incidence is very important prevalence is just giving a percentage okay so this will give you a clear idea about prevalence can just see this as an onset this is death and this is recovery so period prevalence you know there are ten percentage what happened was uh many cases that is one two three four five people died during this period and one two three four people uh newly came into the disease so so still it is 10 cases per total population so we kept total population as standard so this is period prevalence we had seen one example here okay so it's just a graphical presentation and this is very precise uh graph is to explain this point prevalence and period prevalence so denominator will be total population okay in cumulative incidence also total population but they will consider only cases so suppose if we take incidence of october one to september 13 what happens is you check the new cases one two three and four then you have to calculate the person time here so this will be four new cases four new cases during this period okay see one new case two three and four four new cases then we have to calculate the time person here or time month or time week so we can calculate in any way so that is different uh with the incidence okay so prevalence and incidence can be expressed in this in this picture okay so prevalence is the total cases okay this will include old new cases so incidence is a new case from the tap the new cases are being into this prevalence okay so prevalence some will be recovered or some will be cured some will be cured or some will be died so you can see that here some people recovered some people recovered some people died here some people died so that will change the prevalence because on the recovery and on the death here people were changing okay so our death and our recovery the prevalence will reduce so incidence and prevalence there is a relationship that is prevalence will be prevalence is always high okay this is very huge prevalence is incidence and duration so incidence if it is 10 cases per thousand population per year so incidence will be always like this okay this is time person here this is time frame population and new cases prevalence will be a total duration five years it will be 50 per 10,000 or five percentage okay so you can calculate if one is missing if prevalence and duration is there you can calculate incidence and same likewise so we have completed the morbidity measure that is prevalence and incidence and it was proportion and rate actually so tools of measurement were rate ratio and proportion okay good morning everyone we'll be continuing the classes on epidemiological study designs we have so far completed the designs and descriptive epidemiology in detail so today I'll be explaining about analytical epidemiology so we have covered the hypothesis so in descriptive epidemiology we have made a hypothesis so to test the hypothesis or to know whether it is true or false or to reject or accept hypothesis we conduct analytical epidemiology so let's go into detail about the analytical study design so in analytical epidemiology we'll go to the steps or the types before the steps we have two types of analytical epidemiological studies as I mentioned the study remains a case control study and cohort study and when these studies were used are also depending upon the nature of the study okay so this is basically a second major time the first one was descriptive and the second major type of epidemiology which actually focus on individual within the population but whereas the descriptive epidemiology we're focusing on a group of people for a population okay so population descriptives we were collecting now we are going to check the individual perspective and the most important thing is testing the hypothesis rather than formulating so descriptive study we were checking the hypothesis now we're formulating the hypothesis but in analytical study actually we are testing the hypothesis and rejecting or accepting it okay so the first one is case control study it is also known as retrospective study because it is going backwards because the cases and controls are already present at the beginning of the study so the collection of data by asking questions or questionnaires or their previous data their hospital records so it is a retrospective study but it is also known as straw hawk study you can see it as the reverse of cohort C O H O R T so it is going backward so cohort is always a prospectus study so opposite of cohort is straw hawk so case control studies also known as straw hawk study so question might come it as straw hawk study so never get confused with cohort study straw hawk means case control study so the basic thing in case control studies both exposure and outcome have already occurred before the start of disease so before that you need to understand what is exposure what is outcome what is effect and what is course so the exposure and course are same outcome and effect are same suppose we'll take another example of a few people or a group of people who are eating food from street side and who are eating from home and the chances of food poisoning so our exposure is eating food from the street side because it has more chance to cause food poison so the exposure or the cause is always eating street food and outcome or the effect is food poison okay so always an exposure will have an outcome and it is the same as cause and effect and outcome are the disease okay the exposure and cause are the reason for it so exposure is the particular reason for creating that outcome or a particular disease so here eating street food okay is the cause so in analytical study the fundamental thing is we always keep a comparison group unlike descriptive study in descriptive study we don't have any comparison group because we are just describing it we are formulating a hypothesis so here we have a comparison group that is control study so the basic thing in case control study both the exposure and outcome already occurred before the start of the disease if you are going to check the food poison of patients in a city or in a town in a village the exposure and outcome people who have already consumed street food are there and many of them might have already developed the disease so the exposure and outcome have already occurred and the study is always going from disease to cause because we are trying to find out whether actually eating street food cost food poison so that is food poison to its causes we don't know whether it is just a hypothesis eating food from street side cost food poison so we want to know whether it is actually true or false so we will be asking questions to patients that is people who have food poison whether they had consumed more food from street side or they eaten from home and we will compare it or do analysis and we will find out whether the hypothesis is true or false so it is going from the effect to cause but whereas in cohort it will be opposite that will be dealing later so this is a basic design of a case control study okay so there will be cases and controls are already present cases means people with the disease control means people without the disease how we select control will be coming in next slides so to cases and controls the epidemiologist will ask questions and find out how many of them were exposed to our particular cause that is street food case is food poison so people with food poison will be checking collecting data using questionnaire or any method and find out how many were exposed to street food and how many were not exposed to street food at the same time controls without the disease also will be collecting data that how many were exposed to the street food or how many are eaten street food and home food so always we need to have a control group so our assumption is people who have eaten street food will have more chance to get the disease that is food poison than the people who have eaten less from the street side that is we are keeping the controls for comparison so in cases and controls cases are with disease and controls are without disease to the both groups will be asking questions whether they have exposure or they are not exposed because exposure is tobacco chewing whether they chew tobacco or not chew tobacco so our assumption is in cases the exposure factor will be more compared to controls so that is our assumption that is what we are trying to prove in the cases the exposure will be more compared to control so that is why we are keeping a control group because in control group without people without the disease will not be having much exposure compared to the cases people who have the cause will be having more effect people there is no effect means the cause will be less or less cause less effect or less exposure less outcome so the basic steps in a case control study the first step is selection of cases and control then we have to match it then we have to measure the exposure finally analysis and interpretation so how to select cases and how to select controls okay selection based on the diagnostic or eligibility criteria we have to keep a diagnostic criteria and we have to keep an eligibility criteria and we can take from hospital or general population whereas the selection of control control should be from hospital if the cases are from hospital and you can take controls the relatives of the cases or from the general population and the number of controls that is usually the optimal range is one is two one the ideal ideal is one is one not optimal ideal is one is one if you're taking one case you should have minimum one control and if there is more control the study will be good and you can have as many as four maximum of four beyond four it is not much effective so at least you need to have one control for every case and while selecting this control you need to follow a step called matching that is every control should be matched based on the age and gender suppose if you're taking a patient with cancer that is a male patient with 40 age you need to select a control with the same age and same gender but without the disease so that is what is known as matching you have to match age and gender so control will be matched based on age and gender so the third step is measurement of exposure so how do you measure the exposure because you have already produced cases and controls okay now you need to ask about their exposure history so in this case that is if you see the case of oral cancer the exposure is smoking or tobacco chewing so we have cases and control groups so we'll be asking the same questions to cases and controls the exposure factor how many times you chewed how duration we're using these what were the symptoms same questions will be asked to cases and controls so you can use interviews questionnaires past records such as hospital records employment records can take clinical or lab examinations and you need to take the records that is why it's known as a retrospective study because we are going backward so we are asking the past history of cases and controls so most commonly this is the easiest analytical study because only you need to check the past history past medical history and past exposure history of a person but one thing is you need to ask the same questions to cases and controls but the main problem is the investigator should not be knowing the cases and control groups identity if he knows the cases are cases the people are cases if people are cases what happens is he might have had a chance to ask more questions and report more more actively than the control group because control group will not have so much answers because only cases will be having more detailed report of their past history because they are having the disease controls is not having the disease so when you ask the same questions how long you were using the tobacco means they will not say I have not used so the same questions if we ask the investigator shouldn't be knowing the participants are belong to which group this process is known as blinding and the case control study it is very difficult to do the blinding and there will be bias so blinding and bias will be checking in future classes okay so the last piece analysis so how do you analyze the exposure in groups okay so what we are checking here is exposure so how much exposure did the cases and controls had so the measure of exposure is done by a method known as odds ratio in case control study okay so suppose the same example of food poison the contaminated food or the street food okay that is exposure of cause of disease and outcome or effect is food poison this is our hypothesis contaminated street food causes food poison so we select cases that is food poison patients and we select relatives neighbors friends of these people we have more number of controls that is 35 cases and 82 we can have 1 is to 4 ratio that is 35 to 120 up to 140 controls we can have but this is a scenario we have 82 patients so out of 35 cases 33 patients were exposed that is 33 patients were taken food from street okay in control 82 controls 52 people 52 people 55 people were taken food from street but you have to see that the number is more okay you have to check the exposure rate in cases just two people have non-exposed that is not taken from street but in control 27 people are taken so if you see the ratio in cases the exposure rate is 94 percentage and control rate is 67 percentage because the exposure rate in among cases that is vertically you have to see exposed people 33 divided by the cases that is a by a plus 33 by 35 this is 55 by 82 so you will get 94.2 and 67 so the exposure rate among cases is 94 and exposure rate among controller 67 that is what we are trying to prove the exposure rate would be very high among cases compared to control because people who are exposed will be having high chances of an outcome because people who ate food from street side will have more chances of having poison compared to people who are not eaten from street so don't get confused after seeing this number because here it is almost triple amount of controls we are taking so only thing we need to check the exposure rate so sorry so in control the exposure rate is 55 so horizontal line is exposure rate okay this is exposure this is non-exposed this is cases and this is controls don't get confused and always see x axis we write cases and controls phi axis we write exposure and non-exposure okay so this is 55 by b plus t b by b plus t that is 55 by 82 okay 67 so exposure rate is always 5 cases okay compared to controls so odds ratio it is very simple we are trying to find out the odds of exposure among cases and controls odds means chances so what are the chances of exposure among cases and also so odds of exposure among controls the formula is very simple you have to do the cross product that is a by c divided by b by d so it will become mathematically ad by bc that is ad by bc you have to take the cross product so ad by bc is the odds ratio it gives the odds of exposure to the cases and controls we will be asking about their previous exposure odds so this is the strength of association between the risk factor and outcome risk factor is eating street food then the home food outcome is food poison so it gives a measure of strength of association so when we multiply it 33 into 27 by 55 into 2 you get 8.1 so that is the measure of strength of association of eating street food and the food poison that is exposure and outcome association so in simple words we can say that street food eaters had a risk of developing food poison 8.1 times than that of restaurant or home eaters so people who had eaten food from street side will have 8 times chance of getting the disease compared to the non exposure group here i put restaurant eaters don't get confused restaurant or anywhere or home side because we have only one exposure and one outcome in any study okay so our exposure factor will be based on the hypothesis so our hypothesis was like for this case eating street food could be the cause of having food poison so street food eating will have an 8.1 times of risk in developing food poison compared to the non exposure group so here also i put some different this is a hotel name Sagar and Malabar so don't get confused with the names only worry about exposed and non exposed okay so that is all about case control study okay so in cohort study the thing is everything changes because cohort study is a forward-looking study or a prospective study so you remember the word drawhawk cohort is forward-looking study so the backward-looking study is case control or it is also known as drawhawk study it is prospective study or longitudinal study with follow-up we had seen in the designs or you can say incidents and incidence prevalence will be coming later so cohort what is a cohort cohort is nothing but a group of people who have a common characteristics okay so the thing is it is proceeds from cause to effect okay the cause comes first if it is in case of our food poisoning we will be observing people who are eating from street side and we will wait for the effect because at the beginning of the study all the people are without the disease that is the most striking part of cohort study but in case control study both the exposure and outcome have already occurred but in cohort study not the exposure not the effect has already occurred so we will wait for the exposure and the effect to happen in future time so that is why it is known as prospective longitudinal or forward-looking study so you can see that study starts present today it starts not yesterday today it starts so in case of retrospective or case control study we will be asking their past history what happened in previous time but this is forward-looking study so if study starts today now the population is free of disease today nobody is having disease okay so what we do is we observe this group of people so what happens is we observe group of people that is we observe a group of college students and the factor that is causal factor they develop in future time okay so many of the students will be eating from street side and many are not eating from street side they are eating mess food okay so that group a common group we will take some maybe 100 students will take so 100 many will be going street side for eating food and many will not be going street side for eating food and we will keep on observing them so at the beginning of study they are not even having factors that is exposure factor however the period they will be becoming two groups that is one is exposed group and this is non-exposed group the same scenario in case control study what is happening reverse because in case control study it started from disease and no disease exposed and non-exposed in core study it is going opposite that is study without disease few will develop the exposure group will go into exposure and few will go into non-exposure group and people who eating food from street develop food poison few may not develop food poison people who eating from mess that is without exposure factor few might develop food poison few might not develop food poison but our exposure or a hypothesis is to test the cause and effect that is eating street food will cause food poison so in the last year there was a history of food poison during the month of April May so this year we are planning to conduct a study on a group of people or a hostile group so that will become a perspective or a cohort study last month there was a history of food poison for hostile people or a group of students so we were conducting a study on the people and we were like asking questions that becomes case control study so same group we can do different approach but in cohort study study will go to the future it is a forward looking or prospective study at the beginning of the study there will not be any cases so it is time consuming and it is very difficult to follow up because we need to follow up the students follow up the participants okay so this is how it is in design case control and cohort study so there are three types of cohort study one is prospective cohort study retrospective prospective retrospective and combination so I can give an example so this is prospective study so today we are starting the study so there is no a group of people without any disease few are exposed few are in non-exposed group and some of will develop disease minor develop disease same in with the non-exposure group outcome has not yet occurred at the investigation weekends okay so this is a very clear cohort study very particular or very proper cohort study so retrospective so you can see that if we started in 2008 don't confuse don't confuse with the study if we starting the study at 2008 so population without any disease over a 10 period of time all where group of people without any smoking habit over a 10 period of time they develop the habit that is exposure group that is few are started smoking habit and few are not developing smoking habit okay the same group of people we are following for 20 years so this is a prospective cohort study are also known as concurrent cohort study so in 2028 the people with exposure that is people with smoking habit few develop disease and some did not develop disease that is lung cancer and also a non-exposure group few develop disease and few did not develop disease so you might have that doubt that how come in non-exposure group the lung cancer will develop that is what our aim is you have to find out an association between this exposure this smoking habit and lung cancer so obviously we are postulating we are hypothesis in that the exposure group will have more number of cases and non-exposure group will have very less number of cases and obviously it is a multifactorial disease we don't know which factor causing lung cancer our assumption is that smoking causes lung cancer so people with smoking habit will have high chance of lung cancer that is what we are trying to prove in hypothesis because there are 100 people with smoking habit they all will not develop lung cancer and 100 people without smoking habit some of them might develop lung cancer but when we compare the smoking and non-smoking the lung cancer development will be very high in smoking group that is exposure will have a big impact in creating the decision that is what our hypothesis was so we are trying to prove the hypothesis okay I hope that concept is clear so this is a different thing that is retrospective cohort study okay so what happens in the retrospective cohort study don't get confused with retrospective study that is case control study here the study in retrospective cohort sphere of historical cohort for non-concurrent prospective study tastes like we are collecting data from the history okay so if the study is like this that is we are going from here to here to known as retrospective study and if we conduct the study in combined fashion that is historical cohort present scenario and the future follow-up that will be known as combination of retrospective and prospective study so basic elements are similarly case control study we have to select the study subjects we are to obtaining data on exposure so we were checking also of exposure here we will get exact data on exposure because since we are going forward it is a prospective study then we have to make a comparison group then we have to do follow-up and analysis so first thing we have to compare we have to select the population depending on hypothesis we can get general population or special population and we have to get the data second step we have to get the data from records medical examination that will be in a follow-up manner not from their past records we have to do a past records when it is a retrospective cohort study that is a combination the same cohort will be checked for its records the difference from case control study is when we are checking retrospective cohort they are not developed into a actual case they are just developed into exposure and non-exposure group but in case control study actually we are checking the exposure of cases and controls but here we have exposed and non-exposed group when we are doing a retrospective cohort study not the cases then you have to select a comparison group we can take an internal comparison or external comparison group or with general population you can take any comparison group so it will automatically form a comparison group when we follow a population for a period of time some will develop the habit some will not develop the habit so people will not develop the habit will become automatically comparison group that is a cohort within the same group sometimes it will not be available so we have to go for an external comparison then you have to do the follow-up that is the main problem of cohort study when you are doing follow-up the same population will not be there some might have already left out of the city some might have gone for some other purpose so there will be always attrition problem attrition in research says that loss of people during a period of time so there will always an attrition problem in cohort study because maintaining the same sample over a period of 10 years and 20 years it's very difficult so it is very complicated study basically conducting a prospective cohort study it is very difficult it is administratively very complicated it is expensive time consuming but it is very good study because we will get the exact data we need not to relay on the past history or the data of patients we can relay directly we can see the patients outcome in front of our eyes because we are following up we can check it and we can count the actual risk rather than the odds ratio odds ratio is in direct way of checking the risk so it is periodical medical checkup and hospital records routine surveillance and questionnaires in many ways we can obtain the data so the obtaining data is same but in case control study we will be checking their past data which is already being written one will be asking their past history this is like follow-up because we have seen patients without disease and over a period we'll be asking questions so in analysis part what we are checking is incidence okay so the same table with disease without disease and this is exposure and this is not exposure it is non-exposure because the disease will be on x-axis always and the exposure will be on y-axis so for 10,000 people 45 and developed for poison and the remaining 9,955 did not allow comparison non-exposure group only five dial up disease so the remaining were not developing disease so the incident rate we calculate this will go horizontally that is among the exposure rate okay the case control study exposure rate was calculated like vertically a by a plus b here it will be a by a plus c that is the presence of cases among the exposed group that is exposed group is a plus c presence of cases that is 45 cases among 10,000 that is 4.5 and the number of cases or the incidence of cases or presence of cases among the non-exposure group there it was among the cases and among the controls in case control study here it was among the exposed group and among the non-exposed group the difference you need to understand okay this is prospect here that was retrospective in retrospective it was going among the cases and among the controls here it is going among the exposed group and among the non-exposed group so 45 by 10,000 and 5 by 10,000 45 by 10,000 5 by 10,000 so you get 4.5 is the incidence among exposed and 0.5 incidence among non-exposed so the relative risk formalized incidence of disease among exposed that is horizontal and incidence of disease among non-exposed so you get 4.5 divided by 0.5 is 9 so it implies that there is a nine times risk of development of oral carcinoma in tobacco juice compared to non-juice or nine times risk of getting food poison among street food compared to home food or restaurant okay so analysis is different but the meaning is same there we calculated the odds of exposure odds of exposure here we calculate incidence of disease among exposure and non-exposed group is just opposite so the meaning is same it is measuring the strength of association between exposure and outcome or the causal factor and the disease so basically if relative risk is one then we can say that there is no association between this exposure and outcome that is eating street food or have have getting the developing food poison there is no association if it is more than one we can say that because of eating street food the food poison has happened if it is two we can say that if it twice risk okay so greater the strength of association the greater and greater the relative risk greater the strength of association between the factor and the disease or the cause and effect and one example of another type of risk is attributable risk or contributable risk it is also known as risk difference calculated by like this incidence among exposed minus incidence among non-exposed divided by incidence among exposed it will be presented in percentage so 4.5 minus 0.5 divided by 4.5 we get 88.9 percentage it says that how much is the attributable power of that suspected factor if hundred people are eating street food how many of them will develop food poison so the power of that particular factor before relative risk we were seeing compared to the non-exposed group what is the risk of exposed here we are saying the power of exposure or the risk difference or what is the attributable risk of that particular causal factor so we can say that almost 89 people will develop food poison if he eat food from the street side or if hundred people have smoking habit 89 people or 88.9 people will develop oral cancer so that is the attributable risk or risk difference and one last thing is population attributable risk so it is what is the population effect so it is like incidence of the death death in the total population this is only different thing coming here so total incidence of disease so all the deceased incidence we have to include here in total population minus incidence due to the non-exposed so total population death minus population death happened due to non-exposed so what is the incidence in non-exposed group divided by incidence in total population so it also will give a percentage it provides an estimate that if the suspected factor is removed from the population how much percentage of people will be saved if suppose after calculating this we get 40 percentage that is if we remove the causal factor of remove the street food if we stop all the street food vendors we can save the total food poisoning this is 40 percentage so if it is a smoking and lung cancer if we stopped stopping if we stopped smoking habit or if we prevented smoking we can save 40 percentage of the total death so that is all about population the formula is different incidence of total population minus non-exposed incidence divided by incidence of total population here it was incidence of exposed that exposed to become total population incidence of due to all the disease so ultimately we get the disease of people with only exposed so this is a little bit complicated it gives a population attributable risk it is mainly helping in preventive programs if you are doing a mass program on tobacco banning of tobacco counseling that program will have this much effect if we remove that particular factor from that population this much incidence can be saved if a population attributable risk is 70 percentage for the tobacco and lung cancer please stop smoking we can save 70 percentage of the death due to smoking from that population okay that's all about case control and coercity it was a lengthy session but try to understand the concept both are different but one or same it has a same scenario one thing is going backward and one thing is going forward the risk estimation also it is just the opposite one is like measuring the exposure other is measuring the incidence among exposed group okay thank you matching we have two definitions so let's study this definition given by Leon Godis it is a process of selecting controls so that they are similar to cases in certain characteristics such as age sex race socioeconomic status and occupation so why we are doing matching is we need to keep equal parameters such as age sex race socioeconomic status and occupation in both cases and controls so it is not easy way because the age sex race socioeconomic status and occupation such parameters would be different but we need to keep in such a way that we select controls similar age or similar sex or similar such parameters so that we can assume that they are differ only in the presence or absence of a disease otherwise what happens is all these characters or all these variables or all these attributes will act as a confounder or the third variable if a confounder is there in the study it will distort the result we get will not be the actual result so we need to control the confounders so we need to keep the same effect of all these variables both in cases and controls so that we can assume that they are differ only in presence or absence of disease because anyway control is people without the condition or the without the case okay so let's see what is design of matching okay so matching variables such as most commonly we do age and gender matching if we are doing a matching based on the age that is if you have a case of five years we need to always make sure that we select controls with that same age so controls can be most commonly matched in two types one is individual matching and another one is pair matching so individual matching is very simple sorry individual matching and group matching individual matching is just like we are selecting a person with a disease so we can select a person without disease but with the same age or same gender or same socioeconomic status or same race that is known as individual or paired matching so we can do paired matching when there is two controls which are usually matched to each case so we can always we take more number of controls the ideal ratio is one is to four but minimum we need to have one is to one but group matching is a different strategy we select a population of controls such that the overall characteristics of the case suppose if 15 percentage of the cases are under age 20 okay so we are considering only age parameter so the controls also should be under 20 because same group that is 15 percentage controls we select under 20 age because cases are 15 percentage are under 20 so that process is known as group matching or frequency matching so we have two types of matching individual or pair matching and group matching so what are the mistakes we face or the problems we face that is over matching that is matched only on factors not because of the disease suppose we are thinking that age could be the factor which can act as a confounder so we are not addressing other parameters such as gender socioeconomic status so we purposefully neglect all those factors and we concentrate only on one parameter which we would have thought that could be the contributing factor or that could be the confounder so that process is known as over matching we need to match as much as possible variables so what happens is if we do power analysis by matching more than one control in general the number of control should be less than four because of there is no further gain of power above that because maximum is one is to four so there is no point of keeping more number of controls so over matching has no statistical power also because the maximum power we obtain with four controls per case and so other problem is individual matching on too many variables is time consuming costly and cumbersome and may lead to two less controls so if we have if you are trying to match with too many variables like age gender race socioeconomic status income what happens is it will become very confusing and may lead to less number of controls because too many variables are coming up and which we cannot explore possible association of disease with any variable on which cases and controls have been matched so what we have to do is therefore only factors which are not to be associated with disease are studied so we don't need to match for all the variables and over matching also have problem like matching on variables other than those are risk factors of the disease either in a plant or inadvertently we are doing over matching just one example back in a study of ocp use that oral contraceptive pills as a risk factor for cancer if you use best friend friends as controls it is most likely that the controls would also be ocp uses in effect we would have matched for the very factors we want to study suppose the controls if we are selecting they should be without this factor but if you are selecting controls from the best friends they also will not be considered as controls so as the name as the title suggested biases and unavoidable is an unavoidable error in a research so you just cannot do a perfect research a perfect scientist or a perfect investigator uh is is always at a risk of producing error so it is unavoidable 100 percent perfect results you will not get in any study so let's see the bias in research so in this class actually we are seeing bias in case control and chord study so those studies are more subject to the attack of bias and descriptive and experimental so descriptive studies there is no comparison group so that itself is a big bias so an experimental group also there are many types of bias will arise but the most commonly affected study designs are case control and chord study okay so bias is nothing but it is an error but it is in a systematic error not a random error so there will be basically two types of errors the one is systematic and one is random error so suppose let let me tell you one very simple example so you'll get get a idea about bias so if we are trying to measure uh blood pressure of 100 people okay so it's part of our study so blood pressure is being checked for 100 people so by mistake you get a phone call in between and you you didn't record one person's reading properly so that is a random error so it might have happened that chances of therein all the studies are all the investigation and all the reading measurement but what happens or what if the machine itself is wrong so the entire 100 people are giving wrong results the machine itself is wrong the BP apparatus you're using is wrong it is giving plus 20 millimetre mercury extra measurement so the all 100 participants is getting or producing different BP so that is bias it is a systematic error the error will be systematically repeated okay so basically we have three types of bias the first one these are the three common types okay so the first one is selection bias the second one is information and third one is confounding bias so selection bias is nothing but uh when we select participants into case and control or into cohort group if there is any problem arises there it will result in bias second one is information there will be a lot of collection of information in all the studies so if anything goes wrong in information that becomes information bias and last one is confounding our third variable bias okay we'll come into detail one by one so first we are seeing the bias in bias case control study the first one is selection bias in case control study so this is known as prevalence incidence or selective survival bias this is commonly seen in case control study as a result of selective survival among the prevalent cases the prevalence incidence bias uh in cancer type of studies the cases will be of the recent type and a very old type that is an incident case and a prevalent case okay so it is a since it is a case control study the cases which we included must be of a recent origin or a long-standing chronic disease which you must have suffering for 10 years or 7 years okay or uh it could be diagnosed with cancer last month okay so all will be considered as case so what happens is when we include both the type of cases in same study that is this incidence case and prevalence case in the same study the response will be different so this type of bias introduced into case control as a result of selective survival among the prevalent cases okay so prevalent cases means the cases which are being cases for a particular longer period of time okay that duration is a factor uh producing bias so in selecting cases we have we are having a late look at the disease so the disease will be uh seen among the patients or diseases uh occurred uh among the patients at very different point of time so that uh will create a bias and bias in our case control study that is known as selection bias that is particularly prevalence incidence or selective survival bias okay so the second one is second type of uh selection bias is admission rate or berkinsonian bias so this bias is uh named after doctor joseph uh bexton okay joseph bexton bias berksonian bias so who recognizes problem because most of the case control studies will be done at hospital because the cases will be always at hospital cases will not be at uh public so once he's diagnosed as case he'll be uh admitted or will be going to hospital but actually it will not represent the general population scenario because you cannot expect the same number of cases in the public okay because the hospitals are always over represented with the cases so such type of bias is known as uh berksons bias or berksonian bias so second type is information bias the bias which are raised when we collect information from our case and control so the first one is memory or recall bias so we ask questions to controls and cases the same questions we asked about their past but what happens is the cases are having uh more chance to respond to your questions because they are actually having the disease not the controls the same questions you ask to the controls they will not have much to say about the disease because they are not having any disease so that way it creates a problem that type of bias is known as memory or recall bias and the information bias another type is telescopic bias this is a psychological phenomena telescopic effect when people ask about our recent uh incidents we may tell them the events which might have occurred very past so the telescopic effect is uh can be seen up to three years so it will be a shift in events between uh these three years that is ago things which happened uh past three years will be reported as recent events and the recent events will be reported as very past events okay so that is a psychological phenomena when people are asked about the recent past they uh might report the events which occurred very long ago okay that is a nice telescopic bias another type of uh information bias is interviewer's bias okay for exposure to suspicion bias if the interviewer who is doing study knows about hypothesis he will definitely try to change the result because he is up to a mission of uh proving his hypothesis okay so ultimately he wanted to prove something so in that direction the responses will go off definitely he might be changing some responses or there are high chances of uh changing the responses of the cases so that type of bias is known as the interviewer's bias because interviewer knows the hypothesis means it will lead him to question the cases more thoroughly than control if he knows whose cases whose controls and if he knows the hypothesis definitely there will be chance of bias and the last one is hot on effect or observe a bias this is also a uh psychological phenomena when we know that we are being watched we uh give or more effort in our classroom also when uh teacher is uh watching us we try to study well or study more at least we pretend that we study well that is the observer bias when the case and control when a participant is known as they are being studied they automatically try to alter their response because they are being watched or they are being observed that is known as observer bias or hot on effect when human subjects of experiment change their behavior simply because they are being studied so the last one is bias due to confounding of third variable bias this is a very crucial bias so suppose um a person who is consuming alcohol chance of uh congestive heart disease congenate congestive heart disease okay so we are studying our independent variable is alcohol consumption and this is our dependent variable this is cause this is outcome okay so we are studying this case control study we will get an odds ratio and it says that seven times risk of people who consumes alcohol to get this disease but what is we are missing here is the same person is a habit of smoking that we are not taken into consideration okay the smoking has an effect on this heart disease at the same time people who consumes alcohol tend to smoke more regularly so this effect we didn't take so this effect is concealed here the total seven odds ratio the strength of association is not true but it is concealed the actual effects because the third variables are into action so this third variables effects are to be considered when we are doing your research so if you are removing or if you are not including the variables which could affect both cause and outcome that might create a bias which is known as confounding bias so these all third variables which has an effect which has an effect on in cause and outcome on as confounding factors and the bias arise due to the confounding factors is known as confounding bias or third variable bias okay so in case control study what we do is we do matching to avoid this bias so we know matching age and gender matching individual and group matching so case control study we have to follow matching otherwise this bias will arise so next is bias in cohort study okay the same bias selection information and confounding bias so under selection bias we have non-consent bias and missing data bias so in this class I'm dealing only very few bias bias is a very long chapter it can be taken for maybe 10 10 20 hours so that much biases are there but I'm dealing only with case control and cohort study very few biases so there are many biases which can be explored in textbooks but the main things which are important for us I mean I'm mentioning it here selection bias in cohort study is non-consent bias and missing data bias so non-consent concern biases we know cohort study it is a follow-up study a same group of cohort will be followed up for a particular period of time so what happens is this may arise because the originally selected members of cohort may refuse to participate okay so they have given permission to be under study at the beginning but later they did not give consent so that becomes non-consent bias and missing data bias when we collect when we study records on some individuals are missing or incomplete okay so we take information their complete information is not available or there because it's being followed up we'll be checking the data at regular intervals so they might not be giving consent or they might not be participating throughout the study so the data will be missing so in such cases the information no such cases the selection of participants will be a problem so such loss of participants will create the selection bias which is known as non-consent bias and missing data bias okay so it is due to the follow-up period they give non-consent or the data will be missing so second one is information bias the information which we collect from people is different one example is diagnostic bias so diagnostic bias is also known as diagnostic suspicion so if you know the knowledge of a subject's prior exposure so in cohort study there will be exposure and the beginning of the study will be free of disease then they'll be exposed and they'll develop disease after a period of time so if you know subjects prior exposure that will cause a diagnostic bias so such information will be different between the comparison and this cohort group so that create a information bias because the information we collect will be different because of the diagnosis because of the prior knowledge subjects prior knowledge about subjects I mean knowledge about subjects prior exposure so the same confronting bias will also arise here the factors which affect both both exposed and unexposed group so such factors has to be dealt very cautiously in any study otherwise the relative risk or odds ratio which we get will be totally misinterpreted so confounding or third variable bias or confounding factors has to be taken care so cautiously in any study so if it is not taken otherwise the results what we get will not be proper okay so the last bias is postdoc bias postdoc means nothing but we are not getting some association or the result we wanted but we keep on doing dredging we keep on arranging or keep on trying to keep on doing and the dredging or the data dredging or and then we try to find out um some data to test its significance okay so that is nothing but finding an association by try data data dredging and then using the same data to test its significance so the actual data is not used but instead of the data which might give us a positive result is being used from the original data that is known as data data dredging so it is known as postdoc bias postdoc means after the effect okay so such um bias is also there in called study so we'll have a just to recap um the case control study bias is commonly three types selection information and confounding the selection bias in case controller prevents incidents bias or selective survival then admission or back and sonian bias information which has memory record or telescopic bias or interview bias or photon effect or observer bias and the third one is biased due to confounding or third variable bias in court studies the selection bias is non-consent or missing data bias information bias is diagnostic bias then the confounding bias and the last one postdoc bias uh known as data dredging bias okay that's all about biasing this is a very brief idea about bias which occurs in case controller and court study neither the descriptive or experimental study are clear of any uh biases all that is study reasons will be having n number of biases but we are dealing with very important uh biases in case controller court study so let's continue our epistemological uh classes so today i'll be explaining about the experimental study design so uh we were seeing the observational study design so far in uh last two classes so it was descriptive and analytical now let's move on to the experimental study design so the fundamental difference between descriptive and uh experimental study design is the investigator has a significant role in experimental design is not observing the research instead he is actively participating or he is intervening the research or he's manipulating the research not manipulating in the sense in any other sense manipulation means he is actively putting some trek into the research or he's withdrawing something which they were constantly doing uh so the active participation of the investigator or researcher is the fundamental uh feature of experimental design we're just not saying it descriptive study because a descriptive study it is uh observing the details of the groups either it is a case group or control group or a cohort group and uh noting the details will give you the hypothesis testing but randomized control trial or experimental study the investigator has a very striking role so it is the best study design you can say that it is a best study design to test a hypothesis but the last uh examples like foot poison and the smoking and lung cancer in those cases we cannot do an experimental design because it is very uh unethical to create a group with um smoking or create a group with uh street food so there the only possibilities uh case control or cohort study there we can do only observe it but in uh truck trials or any other efficacy trials relating to new missionaries or any new tricks in these cases we can do an experimental study design because the ethical issue is very um crucial in experimental design so this take an example it is a five milligram of a new trick uh we are checking whether it will reduce the pain in society patients compared to the existing lack of neck so in this case we are checking efficacy of a new trick so this is the basic uh setup for an experimental study design comparison of any new tricks or any other new two methodologies whereas in uh disease cases we cannot do an experimental design because we cannot do an intervention with a disease cannot create the disease uh and do an experiment that is uh unethically unethical and it is not at all feasible so mostly uh the disease uh scenario will end up at analytical study and it will not go to experimental study design so we have to stop the testing of hypothesis at analytical study let it be a case control cohort but uh this type of uh scenario we can do a uh experimental study design so this is uh checking two tricks efficacy a new trick invented is it uh good or uh bad compared to the existing drugs so the common uh design is same for all because there will be uh testing hypothesis always need a comparison group so in experimental study design the population we will divide into two groups and we will check the outcome after a period of time the one group will be test group where the new drug will be uh allotted and a group two where the existing drug will be allotted so this new treatment and control treatment allocation or intervention or manipulation is a fundamental part of or most unique part of experimental study design which is not there in the descriptive study designs and after a period of time we'll check the outcome and we'll compare the outcome and we can uh say that which drug is good and which drug is bad so basically we have animal studies and human studies in experimental designs we know animal studies we might have heard about rats monkeys rabbits so all the drugs will be first tested in um animals then only it will go to the humans uh how uh unethical uh to conduct an experiment in animals it is a matter of question but for the sake of human safety we need to sacrifice animals that's what uh that's what the only possible way to test the efficacy of a trick so first it will be tested in animals and if it is found to be efficacious then the study will be moved to humans and humans also it will go step by step first uh the sample will be very long maybe five ten then it will go to hundred and it will reach thousand and there will be surveillance trial then it will come to the market if it is proven to be uh efficacious even in the surveillance that is uh stage four hundred twelve type four so a few examples we'll see how this experimental studies are came into existence the most famous one is the one which did by jane there was a naval doctor who used to go with the navies navies ships so what happened in one journey was many sailors were affected with one disease known as curvy that affects gums which was caused due to deficiency of vitamin c so till that time there was no treatment for scurvy so what he did was he arranged the participants into six groups and he was giving different different reagent for each group and one among was lime so these sailors were later known as limies so what happened was people the group were getting lemon were recovering in two to three days compared to other groups other groups were not required properly during the study period so he found out that the agent within the lemon could could cure the scurvy disease so this was one of the first experiment happened in science so then the smallpox experiment by edward jane he invented smallpox vaccination so this was it was believed that during his time people who are infected with the cowpox is known to be having immunity against smallpox infection so it was believed that in those times so he wanted to experiment on it so what he did was he took a small question deletion of a cowpox which was his maid was originally infected with cowpox so he took small lesion from the cowpox lesion a small pus he took from the cowpox lesion and injected into a boy he made a wound and injected into it in one week period after this inoculation the boy was having symptoms fever and other symptoms but after one week he was completely recovered he found out that the boy got the immunity then again to check whether he got immunity against smallpox again he inoculated a lesion and a specimen collected from the smallpox lesion and injected this lesion to the boy but the boy was not having any smallpox disease and he found out that the boys got smallpox immunity by inoculating cowpox lesion that was a landmark study and which created the vaccination so this was edward jane did it in 1796 so smallpox now smallpox is completely eradicated from just like polio so these were experiments done by scientist so we'll go to the experimental design the true experimental design is randomized control trial don't get confused with root canal treatment this is a randomized control trial that is the true experimental design experimental means we have to do an experiment not just observing the james lint did experiment he gave he intervened with lime water the gender intervene with the inoculation of a cowpox lesion but in descriptive study there was no role from the investigator was just observing so the experimental study designed the true design is randomized control trial the two experiment we were seeing just now at not randomized control trial because randomized control trial means the group should be randomly distributed randomly allocated there was no randomization in both the cases it was just the pioneer experiments so nobody knew what is a randomization nobody knew the necessity of randomization and the control means there should be a control for the comparison then only we can test the hypothesis so just like his control board study they have comparison group we also need to have a control group and this is the third letter t for trial so this is a trial trial means an experiment investigator is doing experiment so here in true sense a true experiment is rct so in rct the first letter that is patient will be randomly selected we can select non-randomly in james lint experiment it was non-randomly selected and we need to have a control or classical group in james lint experiment all the other groups who are getting um other than lime water were controlled he kept it for comparison then only he can prove it and intervention that is trial so these three letters indicate the true experimental study design that is randomized control trial the other one is quasi-experimental design there is no randomization and there may not be control group so this is also an experimental design so we can say that all experimental studies are not rcts if it is to be rct there should be a randomization of participant that is allocation into two groups and there should be a proper control then only it will become an experimental study is in anyway it is trial if it is a non-randomized or randomized it is a trial because it is experimental design so basically we will see the uh steps of rct so the steps of rct so these are the steps of rct first we need to write a protocol we need to write it we need to submit it if it is uh study it is just planning to be conducted in india you need to get a proper approval from the trial registry clinical trial registry and also you need to get approval from icm or the engine council of medical research otherwise you cannot do a proper rct even if you do it will not be published anywhere so first you need to make a proper protocol then you have to find a population where you want to conduct the study then from the population you need to take your sample population because the reference population will be very huge if you're doing a study on uh toothpaste effective toothpaste you are going as school for to conduct a study entire school children will be your reference population so from the reference population you have to select a 50 or 100 students as your sample then you have to do the inclusion exclusion criteria that is you have to see whether they match your criteria whether they belong to the this age group whether they have this much carries or they have these criterias if they are not matching with your criteria you have to exclude it so you get a sample then you have to do the randomization that is the most important part of uh clinical trial you have to randomly allocate participants into experimental and control group so you may take a cheat method or you may take a lottery that is random number method you have to allocate participant into experimental and control group then you have to do the manipulation or intervention that is giving toothpaste to each group that is different different toothpaste or you're giving only one new toothpaste which you are trying to prove in one group and the existing group in other group and you have to follow up for a period of time depending upon the objective of your study and finally you have to do the assess uh assessment of outcome and you have to say that your hypothesis is true or false or you can say that this is uh efficacious than the existing one or this is not efficacious so this is the basic uh steps of an experimental study so let's see the protocol step that you need to uh write about aims and objectives and what questions you are going to ask and the criteria of selection of uh your participants and uh sample size how much sample you need to collect and the procedure how you are allocating the subjects into two groups and what treatment you are applying and how you are applying and what are the procedures so all things you need to write in your protocol and this protocol is always helping us to prevent bias so bias will be dealing in future classes so bias is nothing but error so it helps us to produce the error okay second is a reference and experimental population so reference population as I told you it will center school children and from the school children we select uh depending upon our sample size a small population that is our experimental population it depends upon the objective of study so the protocol is this is the entire school children from that we select a few people as our population so it is derived from the reference population that is derived from the school children total school children so this is the population that is our sample population where the actual uh experiment is going to happen okay so now next what we do is we uh divide this sample population into study and conclude by doing a process known as randomization so randomization is known as a heart of control trial if you don't follow randomization uh the study will be less quality it will be inferior quality in a clinical trial so randomization it is a statistical procedure so you ask the participants to take a cheat from a box where you made if 100 participants 100 is your sample size you made 100 cheats in a box where 50 will be experimental and 50 will be uh reference or control group so each participants will come and take one uh cheat from the box and hand it over to you so you will enter or your assistant will enter that uh student will go to which group and the student will not be knowing so each participants will come and 100 participants will take one cheat so automatically 50 will go to the study group and 50 will be in the control group but the participants will not will not be knowing which group they are being allocated and that is uh known as a procedure uh it is a single blind okay so blinding and bias will come come come in later classes in detail so now let's uh see uh the randomization procedure it is to uh eliminate a selection bias okay selection bias uh error systematic error so bias is uh nothing but which commonly seen in any research so we divide the group into study and control okay so that is done by randomization so it gives a confidence that the groups are comparable always the group should be comparable otherwise the study result is always flowed we will not get the actual result okay so the like can be compared with the likes always since two participants will be comparable so it is assumed that if we do randomization the groups will be comparable there's no proof for it but it's believed that if you do randomization the two groups will be comparable so what we are getting is by random allocation every individual gets an equal chance of being allocated either group okay so each boy has that chance when until the moment he takes that it he has a chance to be in the experimental or control group so that is the heart of control trip so the next part is manipulation okay so manipulation uh manipulation is we are if we are trying to find out the tooth brush efficacy in one group we'll give one type of uh toothpaste and another group existing toothpaste and we ask them to brush many times and it should be followed up so we'll explain the procedure and we'll monitor it and we ask them to do the same procedure for a particular period of time this is known as manipulation or intervention this is the next step after this next step after uh randomization so third step is randomization second step is selecting reference population selecting reference population and sample population then we are dividing the sample population to study and control group then we are doing manipulation after manipulation you'll follow up so if they are using the toothpaste for two weeks of period then after two weeks you check the uh efficacy by checking using any indices any black indices or ginger indices you can check you have to check it before the study and after the study so the the problem which arises commonly in experimental studies attrition this uh terminology I had mentioned in the previous class that is laws to follow up if you are starting the study with 100 people and you need to follow up and you need to get the data of 100 even after the study but some may not comply with their instructions and some may leave out of the study they have all authority to leave at any point of the time so you cannot blame the participants for not being uh adherent to the protocol so if they are leaving out of the study it might affect the power of the