Hello, everyone. I'm asking for taking an interest in my research. I was asked to come here today and present to help give an example of some of the work that CLSA data is being used for and also as an example for prospective students who are looking to work on this data set as some of the work that is possible. So I'm just going to jump right into it. This presentation is called measuring frailty in older Canadians. It's an analysis of the Canadian logitudinal study on aging and it was my master's thesis which was recently decided. So presenting part of the problem, people in Canada are living longer and the population as a whole is getting older. For the first time last year in this country's history, there were more people over the age of 65 than under the age of 15. That's nearly one in six and there are well documented high healthcare needs for this population. It's not just that people are getting older. It's there's a burden of providing care to these people. That being said, there's a variation in health status among people of the same age. Some people who are 65 are healthier than other people who are also 65. So age doesn't necessarily mean the be all and end all in determining health. As this sort of variation increases, there's a vulnerability to declining health and a risk for institutionalization and mortality and this this variation health status is commonly called frailty. Frailty as a concept is complex. It involves multiple health systems and has been linked to physical health, psychological health and social factors as well and frailty is dynamic. It changes over time. So a person's frailty level can increase or decrease. This concept is useful for a number of reasons. The ability to measure frailty would be good for planning health care interventions as an outcome measure and also for assessing the health resource needs of a population. Unfortunately, there's no consensus on how frailty comes about or how it should best be measured, but there have been several attempts to measure frailty in the past. There's two key main theories, the freed phenotype of frailty and the Rockwood cumulative deficit model. None of these methods has been established as the clear criteria on reference or gold standard for this is the be all and end all way to measure frailty in a population. So the just a little bit of background the freed phenotype of frailty involves five physical factors that can be measured in a in a clinical setting. Slow mobility or walking speed weakness, weight loss, decreased activities and exhaustion and a person is classified as frail or non frail if they have three or more or two or fewer of these criteria. The cumulative deficit model is a little bit different. A person is assessed using a list of health deficits that have to be associated with age. So gray hair would not count because it's well associated with age not the health deficit. So health related factors associated with age and within that category the selection of deficits included is very flexible. As long as you have 30 to 70 items the scales perform more or less the same. So your frailty index score is the proportion of the number of items in the index that person is deficit on or that is present. So the proportion indicates frailty. It's a continuous number. As opposed to the phenotype model, which is dichotomous. So our goal here was to evaluate the level of frailty of participants in the CLSA. We wanted to look at what is the best way we can measure frailty here in this data set and we looked at a bunch of different ways that had been done in the past to try to figure out what was the most appropriate for this setting. Secondary objectives for the research were to study the underlying construct of frailty. So what can we learn about frailty from this data and to identify some of the potentially key factors contributing to frailty and key factors for its measurement? We use structural equation modeling to compare some theoretical measurement models for frailty and we assess the validity of the measurement tools that we came up with. So the data source, just something of interest, is the Canadian longitudinal study in aging. As I'm sure many of you know, it's a large scale population based study covering across Canada. It includes community dwelling, Canadian adults aged 45 to 85 at times enrollment and collects a wide variety of physical, psychological and social health indicators, making it a well suited data set for studying frailty. Participants to be eligible for inclusion had to be able to complete telephone interviews, which is important later on. The tracking cohort specifically was what we used because when we started the study, it was the first data set available. It includes 21,241 participants and is cross sectional, even though CLSA is a longitudinal data set, we only had one time point for the study, so there were some limitations to this, but it was cross sectional data. Since then there has been more data available in the spring 2016 release, so if you're interested in using the data set, there is more than just what I talked about here. The data was collected, participants were recruited primarily through those who had completed the Canadian Community Health Survey and to fill out the rest of the sampling frame. Provincial health care registration databases and random digit dialing were also used. All of the participants completed a 60-minute telephone interview and all of the data that we looked at for this study were self-recorded and recorded by trained research staff using computer-assisted telephone interview software. To access the data, first we developed a research question and looked at which CLSA variables we needed and were relevant to the study of frailty. You can look at the data available online through the data preview portal and sort of look at what data is available and how does this fit my research question. After obtaining ethics approval, we submitted an application to the data and sample access committee and then once that was approved, we received our data set for this research. So the variables that we included were all health indicators that were theorized to contribute to frailty or be associated with frailty. They were chosen based on several systematic reviews that we looked at that were focused on primary research studies that measured frailty using original tools. So which variables had been previously used in an attempt to measure frailty? And we also consulted with some experts in the area of frailty measurement to make sure that we had good content coverage. We organized these variables because there were a lot of them based on the international classification of functioning disability and health that was put out by the World Health Organization. And we made sure we represented all of the ICF domains with the variables that we had. So as you can see, I've separated them into the five ICF categories. We had for health conditions, self-reported chronic conditions, there was an exhaustive list, as well as self-rated physical and mental health. Body mass index was included as was continent and self-reported sensory impairment. Depression was measured using the CESD-10 and satisfaction with life scale was also included, as were reported anxiety and mood disorders. Cognition was measured using telephone interview validated cognitive tests, the mental alternation tests, the rate auditory verbal learning, and then animal fluency tests were all included as measures of cognitive function. Functional status was measured including items from four different established scales. We sort of looked at which items could we include that would measure three key domains, upper body strength, lower body strength and dexterity. So all three of those are covered for various items. Activities of daily living were included as was social participation and the availability of social support as measured by the MOS Social Support Survey. So lots of validated tools and standardized measures for different indicators of health across several different domains was the key factors here. So we decided to construct a frailty index for a couple of different reasons. One, because it allowed us to represent the wide array of data available in the CLSA in a frailty measure and also because the phenotype of frailty index required physical function assessments which were not yet available in this dataset. We didn't have walking speed in grip strength so it was most appropriate to use the frailty index as the primary method of assessing frailty in this dataset. All of the variables that we included were transformed to a value from one being the maximum deficit and zero being no deficit so that they were all equally weighted when we summed them all together to give somebody a frailty index value. So as an example of the coding below you can see that ordinal variables, the worst option was coded as one and the best or least frail option was coded at zero as it was for dichotomous variables like chronic conditions. Continuous variables like the cognitive test score were recorded as a proportion of the maximum achievable score. So if you had the worst possible score anybody got you got one and if you had the best possible score anybody got you got a zero. We ended up including 90 health deficits in our frailty index for the CLSA and all the participants who had data missing on less than 5% of the available indicators were included. So we did a couple, we looked at these scales in a couple different ways. We did an exploratory factor analysis on all of the variables included in the frailty index to estimate latent factors that might be causing or driving the associations between these variables. We used polychoric and tetrachicoric correlations because there were several ordinal and dichotomous variables so this was the most appropriate way to study that. And we observed the factor pattern that accounted for the maximum variance among the observations. This method doesn't have any prior predictions as to how these relationships should play out. We also used structural equation modeling which conversely requires us to specify how we expect these variables to interrelate. So we specify a measurement model and then test the fit of that model with the observed data. And then we make stepwise modifications to that model to achieve the best fit. So our hypothesized based model is organized based on the domains outlined by the ICF and those were predicted associations. All of the latent domains should be interacting with each of the other latent domains. And we predicted that each of the variables would load only on one domain. We had a very, very large sample size with over 21,000 people. We had enough of a sample that we could split the data set into two halves. We used the first half to develop the measurement models and we used the second half to evaluate the fit in an independent sample so that we could test whether or not our results were simply due to chance. We also evaluated the construct validity of the frailty index by testing with predicted associations with some sociodemographic variables we had available in the data set. So our hypothesis for the construct validation was what were the associations we would expect for a good measurement of frailty. So we would expect a good measure of frailty to be positively associated with female gender, increasing age, home care received and assistive device use. And we would expect lower levels of frailty in those who had higher education and annual household income. So those were the hypothesis we made for a priori for the frailty index that we developed. So a descriptive summary of some of the data we had available. In the total sample we had slight overrepresentation of the 55 to 64 age group, but nonetheless each of our age groups were all represented. We had a slight female bias, but 51% to 49%, not too much of a difference to be worried about. Most of our data set had achieved a post-secondary degree or diploma. Around 12% reported informal home care services and, or sorry, around 30% reported informal home care services and assistive device use. Formal home care was somewhat less common given that these are all relatively healthy community dwelling adults. Most people didn't report any falls or injuries as well. So those were rare outcomes. The frailty index when constructed for the population could be calculated for almost 21,000 people. So there were 367 that were missing or had data such that it couldn't be calculated. The maximum value was 0.67. So in this again relatively healthy sample of older adults, the maximum frailty we saw in our sample was 2 out of 3 or 0.67%. The average was 0.14, presence of all of the deficits that we recorded. And if we wanted to try to decodamize frailty, the most common way to do that for the most established way is to use a cutoff of 0.25, and everybody above that being frail and everybody below that being non-frail, we saw a prevalence of 6.9%, and both the maximum frailty and the prevalence were consistent with other population-based studies that calculated frailty index. The results of our exploratory factor analysis, we found that based on the SCRE test, we should retain three key factors that should account for the majority of the variation in sample. And when we looked at those factors, we saw that the first one was dominated by the measures of functional status and self-care variables that we had available. So the physical functioning is what we called this factor. It also included a few chronic conditions as well as self-rated health. The second factor, counting for a smaller proportion, but still a significant one of the variants in the sample, included the satisfaction with life-scale items, self-rated mental health, and a few items from the CESD-10 scale. And these were sort of the positive depressive symptoms related to life satisfaction. So how often do you feel happy? How often do you feel hope for the future loaded on this factor? And the third factor included the rest of the items from the CESD scale, which included how often do you feel sad or lonely, how often do you feel depressed, reported mood disorders. This is more of the depressive symptoms sector. These three factors appeared to be the strongest contributors to the variants among the observations. And these patterns persisted when we stratified the sample by sex and age. So these three factors were present throughout the sample. This is our base model for the structural equation modeling, organized the way that I had discussed before. All of the variables were significant based on the WALT tests. So all of them contributed to the model in some way, and we couldn't take those out just based on the statistical tests. So we still made modifications to the model to improve fit. Highlighted in light blue are the changes that we made. The mental health item was moved to the impairments to body structure and function domain, which makes sense if you consider the association between depression and anxiety, whereas body mass index and consonants were moved up to the health conditions domain with the other chronic conditions that were reported. We also included error covariance terms, which it helps to account for two observations that are more heavily associated than would be explained by their common presence in the model. So an example of that is continence is associated heavily with activities of daily living, which makes sense because ADL includes an item that measures continence. So they were more closely associated than just the model would explain. So once we included those terms, the fit improved substantially, but the important finding from this is all of the variables we looked at to measure cruelty were significant contributors to the measurement of cruelty. As an exploratory analysis, we started removing factors just to see if the fit improved because it's possible that because the sample size was so large, any test for significant contribution would be positive. It's possible that all of the variables were significant because of the sample size and to our surprise, the fit improved up until the significantly simplified model, which included only the depression scale, lower body strength, upper body strength and dexterity. So the physical function items and depressive symptoms, when included with a single latent domain which we termed cruelty, had the best fit as any of the other models. So we looked at this a little further. As you can see by the fit statistics, the ones I want to draw your attention to, the comparative fit and non-known fit index, the closer those get to one, the better the fit of the data. Anything over 0.95 is considered a very good fit. So the modified model with the error covariance terms, but still all of the variables included, had very good fit of the data, but the simplified model was very close to one. This could be due to a number of different reasons. It could be to noise of many different measures measuring the same latent domain. And by simply removing other factors, we've removed some of the noise and we've reduced ourselves to some of the core domains of measuring frailty or the most important factors. And it's also important to note that physical functioning and depressive symptoms were the key domains measured by the freed phenotype of frailty. So we're seeing some commonalities between different measures of frailty that were included. We took this a little bit further to see what if we measured frailty using only those variables that were included in the simplified model. So we included all of the indicators of health included in those core domains and scored them by weighting the items equally based on recommendations from Norman and Strider. So we wanted to make sure that our measurement equally weighted all of those four domains because in the simplified model they more or less made an equal contribution to the model. So the score was weighted and one was considered maximum frailty and zero minimum frailty. So we transformed it to make it directly comparable to the frailty index. Looking at the distribution of both, the frailty index has much less skew and kurtosis, which makes sense because it includes many more items. So the frailty index, while more complicated, is more sensitive to smaller changes among participants, whereas the simplified frailty is much less sensitive. And a high proportion of the population had a very low or zero simplified frailty score. But when using the same cutoff, it identified roughly six percent, six and a half percent of participants as frail. So there was, we wanted to look at how closely do those two scales agree, both the simplified and the more complicated frailty index. Also, I should note that the simplified frailty scale did have some floor effects. So there were a lot of people who had a square of zero, which was not the case for the frailty index. When we looked at correlations with some of our construct validity outcomes, we saw all of the associations were in the direction that we predicted for both scales, and were of comparable magnitude, although the frailty index was a little more sensitive to age and injuries, but aside from that, or sorry, and also income, but all of these associations were still in the direction predicted. And this sensitivity to age is illustrated in this chart here, where you can see in blue the frailty index changes a little more with age, whereas the simplified score was less sensitive. The two scales agreed significantly beyond chance, but the CAPA was still less than 0.8, which suggests that it cannot be confidently said that they are measuring exactly the same thing, but the agreement was nonetheless significant. When we looked at a little bit further, which people do the two scales disagree on, the frailty index was again more sensitive to age, but also the participants who were frail on the frailty index, but not on the simplified scale, had many more chronic conditions than those in the vice versa category. So it appears that the key difference is that if you have many chronic conditions, the frailty index is more likely to classify you as frail, but that is not necessarily the case for the simplified scale. So just as a summary of some of the results, we explored the factor structure of the frailty index, and the structural equation models showed that all of the variables contributed significantly. And our theory of how they should relate had a good fit of the data, which persisted in the independent data set. So when we looked at that data set we had set aside beforehand, we saw a similar fit and similar results to our test data set. Both the frailty index and simplified frailty score showed good constrict validity and had acceptable agreement. Some of the limitations of the study is we didn't have all of the indicators of frailty that we would have liked to include, given that it was a secondary data set, but a lot of those variables like medication adherence are included in subsequent data releases. So that will be available in the future for us to look at. In a cross-sectional data set it's difficult to discern what things are affected by frailty and what things contribute to frailty. We can only look at associations. And the participants who could not be included in the analysis due to missing data were a little bit older and had some more chronic conditions than the people who were included. So we may be missing a vulnerable population that can't be measured using this method. And it's possible that the simplified frailty score might be measuring a component of frailty or a different but related concept and they might not be exactly agreeing. So it could be measuring something like disability. So what we are concluding from this is that the frailty index is simple to calculate and interpret. It was relatively straightforward to conduct one and it performed well in this data set. Also the frailty index method is commonly used and generalizable. So we can compare it to other population studies who are also measuring frailty using this method. We have a recommendation for measuring frailty in this CLSA. And we want to look at including a frailty derived variable future data sets. And for people who want to use the data to study other questions they can have frailty index value included in their data set. The simplified scale was a nice surprise. It had acceptable agreement with the more established frailty index and included similar domains to the phenotype of frailty model. So it still had some content validity. It may identify some important components to measuring frailty and had many fewer variables. So it might be useful as a screening tool or used in studies where the number of variables you can collect from your participants is very limited. But its potential as a screening tool should be definitely explored further. So what we plan to do next is look at the simplified frailty score using longitudinal data to see if it associates with outcomes. So all of the variables we included in the simplified score are available in the Canadian study of health and aging. So that might be the next step for evaluating that scale. And we want to look at the frailty index in relation to prospective outcomes like mortality and hospitalizations. And it's definitely good to compare the sensitivity to change and longitudinal data of the frailty index and of the simplified score. Thank you all for joining me today and listening to me talk about the work. I hope that you learned something and enjoyed it. I'm going to pass it over to Mark now just briefly before we respond to some of the questions. Great. Thank you, David, for this excellent presentation. Certainly it's elicited a lot of interest given the number of attendees. So we do have a couple of questions from the audience that I'll get right to you before I ask some of my own questions. Okay, so we have a question from Dr. Pichet. Was vitamin D intake or status considered for this study? Both the Canadian Community Health Survey and CHMS data for this group are available. And before David, you answered the question, I'll just let everyone know that biospecimen data from CLSA are not yet available. So if this question pertains to biospecimens, you were not able to look at vitamin D in the blunt. There is, to answer your question, there's self-reported data on vitamin D intake and status available in the maintaining contact data release, but it wasn't for the data release that I used. So it had been used to measure frailty in the past, absolutely, but it isn't something that I could include based on the data set I used, but it's something that we'll look at further in the next release. So from Alicia Rodriguez, did you consider to use the CSHA clinical frailty scale to conduct this study? Well, I'm not mistaken. The CSHA clinical frailty scale used a very similar method to construct a frailty index, and their data set as we did in this one, so we're hoping that the results are comparable. And we did see similar results in terms of the proportion of frail participants in the distribution frailty index scores. From Nader, did you calculate phenotype frailty, i.e. the freed frailty index as it seems your index is close to it? We looked at calculating phenotype frailty using the freed frailty index, but it requires functional assessment data, which we didn't have in the data set, so we thought it was better to look at freed frailty score when we have the physical assessment data, which I think is coming in one of the next releases. So we need things like gate speed and grip strength to measure that properly, and when that is available in the CSHA, that's something that we'll look at. Great. Comment from Larry Chambers. It would be helpful if at the end of the presentation a slide showed the items in each of the frailty indices. I can post that with the rest of the slides. I'll add a slide that includes that. It's just a very busy slide, so I didn't put it in, but I can definitely add that for people who are interested in the slide deck that eventually gets posted. Great. Fantastic. From Fatima, the simplified index is the sum of the, oops, I lost it here. The simplified index is the sum of the four items with equal weight. Is that so? That's correct, yes. Because each of the domains in the simplified model from the structural equation analysis had more or less an equal contribution, but each included a different number of items that measured that domain. We weighted the domains equally, so if the depression scale had 10 items, it would still make the same contribution as the dexterity domain, which only had three items. So, yeah, we weighted each of those equally so that we didn't unintentionally measure depression as more important to screening frailty than dexterity, for example. Great. Thanks. So, the next question has to do with missing data. And indeed, that was a question I had intended to ask you as well concerning the missing data. Let's find my question. Right. So, I was going to ask you, David, how you handled missing data? Because you said that you had included people with less than 5% missing data. So, I was wondering how you may have handled the missing data in your analysis. And from Kojo, the question is, why not consider multiple imputation to treat missing variables? So, we looked at doing multiple imputation for the missing data, for the missing variable, absolutely. The main reason we chose not to is the proportion of people with missing data was very small. So, it was unlikely to make a large effect on our results. The other reason being because the frailty index included so many different variables, there weren't too many other socio-demographic factors we could use to impute the data with. So, there was a couple of different reasons. But the most important being the proportion of people with missing data was very small. So, in those people with missing data that you included, how did you handle the missing data? Especially when it came to computing the frailty index, if they had missing data on one of the items that composed your index. So, we didn't exclude everybody with any missing data for the index. As long as they had 95% of the items reported, they could still have a frailty index number calculated. It's just if they were missing three, then their total sum of all deficits would be divided by 87 instead of the full 90s. So, they were removed from contribution to the index completely. They didn't bias the sample to the direction of being more frailty just because they were missing. But previous studies that had calculated frailty index for other data sets found that you lost integrity of the score if you included people with many fewer missing data than the 5%. And so, we followed that same convention. Great. Okay. And yeah, I've seen indices calculated using that exact type of process you described. From Meguna, it seems that your simplified frailty index covers only two domains, physical functioning and depression. It may miss other major domains such as weight loss, et cetera. Could this index be too simplified? I think that's an excellent question. I think if you want to be comprehensive and include everything that could possibly contribute to frailty and make your measurement as accurate as possible, it's been shown that including more items makes your index more accurate because the more you measure about a person, the more accurate your assumption of their health is going to be, or your measurement of their health is going to be. The purpose of the simplified scale seems to be most likely to be a screening tool. So, the goal wasn't to cover everything that could possibly contribute to frailty. It was to narrow it down to the most important or core thing so that we could have at least a crude measurement of frailty in as few items as possible. So, this didn't necessarily state that these are the only things that are important to frailty. Rather, that they have the potential to be the most important things to measure frailty or the most poor part of it. Great. So, a question from another mark, not me. Comorbidity burden is a predictor of outcome. Indices for this exist such as Charleston by including chronic conditions to frailty index, including concepts of coorbidity as well as frailty. Would it be better to exclude chronic conditions from a frailty index? It's possible. Part of the problems with studying frailty is there's so many interrelated concepts. And frailty could potentially include many interrelated things like comorbidity or disability or mental health or anything like that. And so, part of what we looked at and part of the research in looking at the simplified scale is taking out some of those other things that could be heavily related but creating noise in the measurement. And so, the simplified scale didn't include any measures of comorbidity but still seem to be relatively close to the frailty index. So, it is possible that we're including a separate heavily related concept like comorbidity. And so, that's part of what we looked at is taking out stuff like that. So, yes. Okay. From Emuna again, how did you set your frailty index cutoff at greater than or equal to .25? And the follow-up is your index distribution with SKU? Thank you. The SKU was expected for a couple different reasons. We had a relatively healthy sample and so, we're not going to get all components or everybody, we're not going to get an accurate sort of frailty estimate of the whole population. We have, these people have to be relatively healthy community-building adults and they have to be capable of completing a full 60-minute telephone interview on their own. So, we're going to exclude people who are sick, who are institutionalized, who have some cognitive decline to the point where they can't participate. And so, for lots of reasons for our sample, you'll see that the SKU is to the less frail.  The people who can participate in this study are the healthier members of that age group. So, that helps to account for, or that's our hypothesis as to how it was skewed to that direction. The frailty index threshold of .25 comes from a paper that came out a couple years ago where somebody did a free, they collected a large sample and measured both the freed phenotype of frailty dichotomous measure. So, like a fail and non-fail and they also calculated a frailty index. And they saw that the curve of the frailty index value for the robust people crossed the curve of the frailty index values for the frailty people at .25. So, that was the point where it was most responsible to make that cutoff. But if you refer to the original Rockwood paper, he'll say that the frailty index was not designed to be a dichotomous measure and should be looked at continuously. I did that more for transparency. So, people who are used to the dichotomous classification can get an idea of the level of frailty of the participants. Great. From Laura Anderson, great presentation. Could you further describe how the results of the factor analysis informed the final indices that were developed? The factor analysis was a little bit more exploratory. We wanted to look at sort of what are the domains that come up and then compare those to the results of the equation modeling. The results of the factor analysis weren't used to inform the construction of the model. It was more of a sort of a confirmation that both identified similar domains. So, the factor analysis results, the domains identified were consistent with those we found present in the simplified model. But both of those analyses were conducted independently. Great. From Ahmed, okay. He says, good job. Have you thought about doing rush analysis on the index? I have not, but that's definitely something I will look into. Thank you for the comment. Great. So, we'll get to Satya's question in just a second. A couple of questions from me. The MOS Social Support Availability Index, the questioner is one of the items in your frailty index. So, we have about 19 questions. And I was just curious, did you, you gave your description of how you took each item and quantified it for inclusion in your index, 01, or if it was more than one response options, you gave 0.25 points for one answer, 0.5 points, etc. Is that how you took each of the 19 items on the Social Support Availability Questionnaire, scored them and put them into your index? Yes. Okay. So, you didn't give one value for the whole Social Support Availability Index. You gave one value for each of the questions. Each item for the MOS scale and for the other scales that were included was considered deficit on its own. Okay. So, then that's interesting because could you then, in total then you have 90 items. So, 19 of those 90 items came from the Social Support Availability Questionnaire. And you had, I don't know exactly how many, 15, 20 maybe more from the chronic conditions questionnaires. There are around 20 chronic conditions. There was about 16 reports of physical functioning. We didn't pay too, too much attention to worrying about there being an equal number of items in each of those domains. We just wanted to make sure that we had more than one item or representing each of the domains that we looked at. Right. So, then could it be possible that, and I don't know the answer. I'm just throwing this out to get your thought. Could it be possible that you may have in so doing given more weight to certain constructs than others? If you just treated each question, for example, as contributing one to the denominator of 90 in your index, could you not have given more weight then to certain constructs than others? It's possible, but unlikely and it can be something that we look at, weighting, like sort of assigning, look at the number of items in each of those domains and then weighting the domains equally to see if that affects the measurements at all. But in practice for developing it for the index, sort of the standard procedure is to not worry too, too much about domain weights and just include any sort of deficit. So, based on the theory, if you have deficits in any area, they all more or less contribute equally as long as you're measuring enough deficits. So, I feel like that would become more of a problem if you were collecting 20, 25 deficits, but when you get past 40, 50, 60, as long as you're representing a number of different domains, the number of items in each domain becomes less important. Great. And I noticed in your demographic table that the proportion of people with post-secondary education was quite high compared to the average that you would see in the Canadian population. And you usually get that in studies, even the CLSA tracking cohort was a random sample of the population, but you still see self-selection in most epi studies of individuals who are more educated than the norm. So, do you think that this could have created a bias in your results? And also, did you think about using the sampling weights to adjust for that? It's definitely possible that it contributed bias. As somebody else had noted, that the skew of frailty was towards the healthier age group. So, it's possible that there's a sampling bias which exists in any self-reported study that you're more likely, as you had mentioned, to catch people who are more educated because they're more likely to self-select first today. So, I hadn't done sampling, but it's definitely something that we can look at for sure. Okay. And so, another bias-related question from Satya. Thank you for the interesting talk. If frailty is to identify vulnerable older people, then excluding those with missing data could mean excluding more vulnerable participants. And you had mentioned that. How would that impact your findings? So, I'm thinking of potential selection bias here. It's possible that we're missing some people who are the most frailty because they... And we talked about that a little bit in the inclusion criteria for the study, is the people who are able to provide complete data might be a healthier group than the people who are unable to provide data or unable to be selected. And I think the people who couldn't report for a lot of questions, who maybe didn't complete cognitive function tests, who maybe over the course of the 60-minute interview didn't answer all of the questions. It's possible that the people with the missing data through the interview are a more vulnerable group. And so, the purpose of this study wasn't to assess the frailty of everybody. It was to assess the frailty of people who have complete data in the CLSA. It's important for, I think, from a generalizability standpoint to make sure that these results are reported in the context of the group that is sampled. And it's possible that there are many more frailty people, and it might be interesting to look at frailty in other contexts, such as people who are in nursing homes, and not healthier community-dwelling adults. Great. From Peter, thanks for an interesting presentation. I was interested that you included measures of social support availability, so we've just been talking about that, as part of the frailty index. While I understand the ICF model includes environmental factors, in the ICF model, these seem more as predictors of the components of disability, or factors that could potentially modify how disability components relate. Should the component of social support sit outside of the measure of frailty, or are you confident that including it within the frailty index was the right way to go? This is a phenomenal question. That's part of the reason that we did this study this way in the first place, because the original question was, what's the best way to measure the frailty of this population of these participants? And we said, well, how else has everybody measured frailty in the past? And there are people who include only physical function measures and chronic conditions. There are people who, there are scales that include social factors and environmental factors, and consider those just as important. There are social frailty scales that include only social environmental factors. So our goal here was to be comprehensive and to include everything that people have used as potential domains for inclusion in a frailty measurement scale, and to see their relative performance. So we wanted to see how the social support variables contributed in the context of the other ones. And this analysis showed that they did make a significant contribution. So based on these results and the model that we had, the social support survey items did contribute to the measure. And so based on these results, they should be included. But excluding those and other items for the more core items like the depression and the physical function ones that are included in the FREED index also showed promising performance. And so for a frailty index, I think they should be there because the results, that's what we got from this analysis. But if you want to have a simplified screening tool that looks at only physical function and depressive symptoms, then it doesn't need to be there. It depends on what your question is, I think. Great. Thanks, David. I don't see any other questions. So we'll give people a final minute or two to type any last questions if there are any. While we're waiting for that to happen, I should just note that Laura, our communications officer, if you scroll upwards from the last question in the chat, you scroll upwards, you'll see that she has given a link to where the webinar will be available. And all registered attendees will be notified by email when the webinar is posted. So David, you'll add that additional slide concerning the components of the frailty index and then it will be posted. So that was a response to Fatima's question, where can I find the slides? Just again, scroll up to see the link and you'll get email notification of when the slides are posted. From Ahmed, a final question. CLSA includes participants who are 45 years or older, which is unlikely to be included in a frailty study or to be frail. Do you think the index will be different if you include only people aged 65 and over? Thank you for the question and an interesting one that we thought of when we were designing the study. Most people who have looked at frailty restricted their analysis to people aged 65 and over. And if you are just interested in classifying people as frail or non-frail, you'll definitely find more people who are frail in the over 65 age group. I think one of the benefits of using this data set to study aging is we are including people who are in a younger age group of 45 and up. And it is interesting to see if frailty is detectable. And if that population is indeed less frail, then they're the people who are in the 65 and older age group. So it's an interesting way to look at, to see how the frailty of those participants changes as the study progresses. And I think measuring them at a younger age group is going to be interesting as a comparison point and also to see how that changes over time as the CLSA continues. I think there's a number of advantages of looking at the younger age group that isn't necessarily seen in previous research. I would agree with you. People don't suddenly become frail when they turn 65. So it's not like people who are less than 65 years of age cannot be frail. And I think you hit the nail on the head. One of the benefits of the longitudinal component of CLSA is that precisely you'll be able to do exactly what you just said is look at the development of frailty in middle age and that maps directly onto the life course perspective. May Muna's question, clinically speaking, do all chronic conditions have the same impact on potential frailty or can it depend on what chronic condition you may have? And if the answer is yes, the impact of different chronic conditions can have differential impact, coding each condition is 01 and having it contribute equally to the index could be too simplistic. Another excellent question. The way that we did it is weighting all of the items equally. So every item was coded from 01 whether it was including all the chronic conditions but also many other items. And so that's the standard procedure for creating a frailty index. We wanted to follow along with how it had been done in the past and this has shown good validity a number of times. This equal weighting of all deficits, the more common ones, the less common ones, the ones that are interpreted as having a very high impact on health and the ones that are interpreted as having a relatively low one. So the way that we handled chronic conditions in this analysis was the same as that. It was weighted just as equally as self-rated health. So I don't know about trying to assign different frailty weights for different chronic conditions but because we have enough items, weighting individual items is unlikely to change the overall index score of the individual. So if you had many fewer items that would make more of an impact. If you were collecting only 10 or 15 things, the relative contribution of each item would be, I think, more likely to affect the results. But if we're including 90 deficits, the individual weighting for each item I don't think is worth changing because it's very unlikely to affect their overall score. And I think the performance of our score in the population more than supports that. Right. And there's still a lot of disagreement over exactly how to define frailty or how to measure and insert studies. And I think that we're seeing that in this question and also in my earlier question. And you can certainly discuss different weightings for different questions and that will just generate another 20 years of debating discussion as to how to measure frailty. But it's still a valid point. So I think, sorry, go ahead. No, I agree that it's absolutely a valid point. And sort of the way we handled it was including many, many items so that weighting was less effective. Right. So at this juncture, we're going to end the webinar. David, it was a really interesting talk and it's great to see students using CLSA data for their work. You're not the first student to present. But you know, it's fantastic that we're getting students to present their work with the CLSA data. So it was a very interesting and clear, lucid presentation covering a very important topic. And on behalf of all of us at CLSA, thank you very much. And once again, congratulations on your MSc. Thank you everyone for participating and thank you for your excellent questions. Great. So I'd just like to take an opportunity before closing to mention our next webinar. So the theme is going to be primary prevention of cancer and chronic disease through improvements in unhealthy lifestyle behaviors. The presenter is going to be Katarina Maximova from the School of Public Health at the University of Alberta. We'll have more details on this webinar in the coming weeks and it's scheduled for November 1st at 12 p.m. eastern time. So we look forward to having all of you join us again for that webinar. And again, David, we wish you all the best in your future endeavors. Thank you very much everybody for joining us. Have a good day.


And so, on the lower level work began. The lower level consisted of British and American experts on transportation, strategy, supply, and the various service arms. They did the spade work, arranged and correlated all the data needed by the chiefs of staff when the conference moved up to higher levels. Shortly before the next level was reached, Prime Minister Churchill left Canada to spend three days at Hyde Park in consultation with the president. Then with Mrs. Churchill and his daughter, Subleton Mary Churchill, he returned to Quebec. On his arrival at the railway station at Wolfe's Cove, he was met by the prime minister of Canada, Mr. Mackenzie King. Together they drove to the Governor General's house at the Citadel. On and the Princess Alice, the representatives of King George VI in Canada, rooms had already been set apart for the president, a simply furnished bedroom, a comfortable and practical study. Here also was the informal conference room, the room in which the most important decisions were to be reached. And now the conference moved up to the higher level, the combined chiefs of staff, the highest ranking officers in both nations. These combined staffs discussed ways and means and prepared their plans. Plans to be submitted when the conference reached its highest level when President Roosevelt arrived. On the 17th of August, the guard of honour of detachment from the Canadian Army, Navy and Air Force, led by the band of the Royal Northwest Mounted Police, marched onto the parade ground at the Citadel, Kenzie King and Mr. Winston Churchill for the newsreel cameraman on the terrace outside the Governor General's residence. They were later joined by the combined chiefs of staff. One chair remained vacant. Suddenly, the drone of a low-flying plane was heard overhead. A flying boat circled in the morning sky. Slowly it turned into the wind and then swooped down to make a graceful landing on the St. Lawrence River. Mr. Anthony Eden, the British Foreign Secretary had flown from Ireland to the conference. His arrival soon to be followed by that of Mr. Hall indicated that important diplomatic as well as military moves were taking shape. Within a short time, he joined the group on the terrace and was welcomed by the President. As the conference entered its final stages, the United Nations waged the new kind of war which Hitler had taught them, the war of nerves. The press and radio told the Axis only what they wanted them to know. Mr. Hall, Foreign Minister of the United States arrived in Quebec. It was met by Mr. Mackenzie King, head of the Canadian Cabinet. It is believed that Mr. Hall came from Washington in order to combine the political aspects of the war with the military plans of the General Staff. The Germans inside their fortress Europe heard only rumors. Not a hint of where the next blow would fall. Today, Mr. Churchill drove through the streets of Quebec on his way to visit the city hall. The English Prime Minister, smiling at conference, the Italian people, their country now an outer bastion of Hitler's fortress heard not a hint of the armistice already in the making. Honourable Mr. Henry L. Stimson, the American Secretary for War, flew in from Washington. The Japanese knew that they too were not forgotten at Quebec. Mr. TV Song, the Chinese Minister of Foreign Affairs, has arrived to join the... Already a new blow was being planned against them. The American Secretary of the Navy, Mr. Knox, arrived by airplane today from Washington. The Secretary appeared confident. The War of Nerves. Did Hitler, Tojo, and the wretched Mussolini remember those days when they had met? Remember how their meetings had killed the hearts of all free men? On August 24th, the last day of the conference, the President and Mr. Churchill granted an interview to the press. What had been decided could not yet be revealed. But as the President was later to state at Ottawa... And we have arrived harmoniously at certain definite conclusions. Of course I am not at liberty to disclose just what these conclusions are. But in due time, we shall communicate the secret information of the Quebec Conference to Germany, Italy, and Japan. We shall communicate this information to our enemies in the only language that twisted minds seem capable of understanding. To break up the gang in order that gangsterism may be eliminated in the community of nations. We are making sure, absolutely irrevocably sure that this time the lesson is driven home to them once and for all.


Well, please be seated. I didn't realize that there was a sort of a hillside here in the Rose Garden that somebody was standing up higher than Well Secretary Bowen and members of the Congress and Hoosiers and Hoosiers at heart. It's a plamed. It's a pleasure to welcome you all here today To the White House to honor the 1987 NCAA men's basketball champs from Indiana University We We figured the Rose Garden would be an appropriate place to host you all today It's a The dimensions about the size you're used to But I apologize. We didn't have enough time to paint free throw lines and put up baskets to make you feel really at home This has been a very special week for you all as players for your coach shy and retiring Bobby Knight And for the people of Indiana I Grew up just one state west of the Wabash River and I know a little something about Hoosier hysteria In Indiana babies aren't born with silver spoons in their mouths They come equipped with basketballs and high-top sneakers. I Don't know how mothers there manage basketball is a way of life in Indiana that many people will never understand But a movie out these days, you know the one with the funny name Hoosiers Along with your example will help to explain this phenomenon. I Have to say also, I have a personal memory of that because back in my day of athletics and playing and then later Broadcasting Indiana was sort of the capital of basketball and many of your high schools played about 40 basketball games a season and no football and Some of those came to the college where I was enrolled and when the football season was over and I Having played some basketball in high school toyed with the idea of now of going out for basketball On the first day at the gym. I took a look at Those fellas and what they were doing with the basketball and I said no I think I'll go in for swimming One measure of Indiana basketball is much like politics and government is statistics Indiana was the co-champion this year in a tough big 10 conference and they won 30 games Including a rather important seven-game run here at the end of the season This is the fifth NCAA basketball title for Indiana But the numbers don't match the thrill you provided to millions of basketball fans here in America You won tough victories for some extraordinary teams LSU University of Nevada, Las Vegas and of course a one-point come from behind victory against Syracuse right before the buzzer This was no series for the faint of heart players or spectators Wasn't for one spectator sitting upstairs in front of the TV set Let me also say that there were no losers last Monday night I think you'll all agree that Syracuse gave you the game of your lives The players and fans from Syracuse I offer my congratulations to you're all winners But let me talk about some individual winners with us today Seniors Todd Meyer and Daryl Thomas have played with heart talent and determination for Indiana for four years and Came through when called for Monday night Ricky Callaway Dean Garrett Steve I'll Joe Hillman each provided us with clutch performances and moments of brilliance So much for the front court. I also want to talk about a couple of guards You know the short guys on squad Keith smart tournament most valuable player Keith wanted to play basketball so badly that after an accident his senior year in high school He decided to grow six inches and play as a walk-on at Garden City Junior College in Kansas and play He did and last year Keith was a junior college all-American This year Keith iced a 16-foot jump shot with four seconds on the clock to give Indiana its national title That's what I call progress And then there's Steve Alfred one of those basketball babies I referred to a moment ago Indiana's Mr. Basketball is a high schooler an Olympic gold medalist two-time all-American The leading scorer in Indiana history and Indiana's team leader And that's just on court Off the court Steve is a conscientious student and a model citizen whose values are as important as his field goal percentage He's my kind of basketball player, and he's America's kind of student athlete Finally, I'd be remiss if I didn't offer a few words of comment about coach Bobby Knight Heck everybody gets to take a shot at Bobby. Why not the president of the United States? Actually I'll leave Bobby's critics to their own devices critics aside one thing is certain Bobby cares deeply about his players on and off the court. He seeks to produce the best in his players through hard work Excellence in execution and selfless team play Now when all said and done Bobby is my kind of coach and Bobby if you've got time one of these days I'd like a few pointers on fly fishing But you know the stats as well as I do Bobby Knight is the winningest coach in Big Ten history He's won eight Big Ten titles He's won American gold medals in the Pan-American Games in the 1984 Olympic Games, and he's coached three Indiana teams to national titles joining Adolf Rupp and John Wooden in the elite three and over fraternity Hard work makes for winners on the court and dedication in the classroom makes for winners throughout life These two elements are certainly at the core of this Indiana team And I congratulate you all for living this example And again for winning the 1987 NCAA basketball championship And now I understand that Doc Bowen has planned a little reception for you at his place So I'll close with what I understand to be a popular Indiana benediction If I don't see you in the future, I'll see you in the pasture Thank you all and God bless you Mr. President, thank you very much for taking the time and allowing us to bring this group of young men here to visit with you this Afternoon, I have here on behalf of the team from really not just the team But from the state of Indiana where I think you well know you have a tremendous amount of supporters a Jacket that represents both the state of Indiana and Indiana University and basketball being a big thing in our state I also thought I'd make it in red our colors when we brought it to you So I'd like to give you this jacket on behalf of really the state of Indiana But I think you'll find this your size We We I think as a basketball team We took a lot of pride in the fact that we were a pretty tough team We came from behind a lot and some critical situations and I think we talked about what? We'd like to say that would be most appropriate to you and on behalf of our players and coaches It would be this mr. President I think you have shown all of us across America what it's like to be tough in critical situations And we have a great appreciation for your mental toughness and your competitiveness in the situations in which you've been involved And I think all of us in this country appreciate that more than anything else And I would hope our basketball team reflects those same ideals as you have during the time You've been here in the presidency. Thank you very much for giving us that Thank you We're a day late. I could have used this yesterday on Capitol Hill Mr. President Just like coach said this is a tremendous honor for all the players and coaching staff and we certainly appreciate you taking the time out to visit with us and letting us have the opportunity to visit with you here at the White House and we'd like to Present to you a team ball autographed ball by all the players and and coaching staff representing the Championship that we just won and Todd Meyer would like to present you with a hat and a shirt again representing Indiana and the title that we just won and Daryl Thomas Would like to present you with an Indiana basketball sweater Now don't get out of my sight Well, thank you all very much and thank all of you and again They're on their way to making it that way should you should you have done more to secure the embassy?


What's up navigation traders hope everybody had a great week of trading? Welcome to this week's video update today is Friday, November 17th Before we jump into the alerts just wanted to make a quick announcement here I updated the how to use navigation alerts video So if you haven't had a chance make sure you watch that It really just sets the stage for how to get the most out of navigation alerts Kind of some do's and don'ts of the use of the alerts and that kind of stuff So check that out, especially if you are a newer member So without further ado, let's jump into the alerts first alert that we had on The 13th on Monday and we had a lot of closing adjusting trades when you when you're in that Week leading up to expiration. You know there's expiration week for the November's options You're gonna we're gonna have a lot of closing and adjusting type trades So not as many opening trades this week a lot of a lot of closing and adjustments. So first one Was an adjustment in adobe and this is where we just simply closed out the 155 160 call vertical This was deep in the money. We just closed this out And still holding on to our 175 180 call vertical along with our full iron condor So if we go to the platform and take a look at adobe you can see, you know, I had that surprise Move up on us and then it's just kind of grinded higher since then starting to looks like prices starting to kind of settle down into a range So we've got this We've got this call vertical So we're looking for a little bit of a down move to benefit that piece And then we've also got this another iron condor on here where we put it on to collect more credit give ourselves some more time to Get backs big get back to profit so you can see prices right here well within our range So we're just waiting on adobe. You applied volatility continues to stay really high finally starting to get a tiny bit of contraction today, but Hopefully we'll see see some more contraction in that implied volatility Assuming price kind of settles down here and hopefully hopefully get back to profit in adobe Next trade I talked about the roll So we've got that 180 185 call spread the one I just showed you Next trade was a rolling adjusting trade in IBM So price in IBM continued to move down. This was originally originally a short straddle And so with price moving down we needed to roll down our untested side So we rolled down our 155 down to 150. So we've got this inverted strangle in IBM And you can see how prices just kind of moved moved down after earnings here So we've got this inverted strangle with the 150 call 160 put looking for a little bit of an up move to kind of get back in that range and We we will eventually roll this to January. So we've got 20 some days what do we got here? We've got 20 28 days in December 63 in January. So we want to wait till this gets down to about 45 to 50 days Before before we roll out of December into January, but by doing that we should collect another nice credit and Continue to chip away at IBM Next trade was a rolling adjusting trade in the S&P 500 This was a short call vertical. This was part of what was originally an iron condor We decided to hold on to that short call spread to keep that short delta some of that short bias in our portfolio And so we simply just roll this from November to December and we roll the strikes from the 2550 2580 To the 2590 2620. So if we go to the platform and take a look at ES see where we're at there This is that position here. So you can see now we hold the 2590 2620 call spread and And we're gonna look to put on another iron condor. It's kind of centered around where price is I was Trying to get filled on one on Thursday when we had that big down day and implied volatility spiked but then Yesterday, excuse me, Wednesday was the big down day Thursday climb back up and pled volatility dropped So I'm gonna hold off on that You know if we get a little bit more continuation to the downside and pled volatility pops back up We'll definitely look to do that And if it just kind of stays stays where it's at will still probably put one on next trade was Closing adjusting trade in soybeans. So we were continuing to work our way nicely out of soybean So he'd put that I see on iron condor took it off took a booked a nice little profit on that piece and in XLV We we did a closing trade in XLV. So we had a butterfly made about 20% Profit on debit paid there. So that was a good trade in XLV XLV is the health care Index so you can see implied volatility is is popping back up today So still to stay nice and high. So we may potentially look at a new position in XLV next week Keep in mind. We're kind of in that no man's land right now We've got 28 days to expiration in December and so it's under 30 days and then over 60 days in January so it's not that you don't want to put positions on here, but You know starting next week will definitely be be starting to put inter positions in January. So look for that Next trade was we bought an iron condor back Closing adjusting trade in ES. So we just had three days left left to expiration This is one where price was hanging out all the way on our upper range. I feel it felt like the whole the whole time Finally got that little move down and we were able to book a nice nice profit on that piece I already showed you we still have the call vertical. So we're still in the ES iron condor But we'll continue to to look for new opportunities to add to that one hopefully next week And then we had another ES trade another s and p 500 futures trade. We had a long put vertical So this is totally separate from our iron condor as I stay here This position is separate from our iron iron condor trade This is one that we just simply put on a couple months ago to add some short bias Some short delta to our portfolio. So we rolled that from november to december So if we go back to the analyze tab and Click off the calls and click on the puts so you can see here It's just we need to we need to we need price to move down to benefit this piece But again, this is in our portfolio for that protection Uh, so if the if the market does make a major down move, we're going to have that that bias I talk about this every time But just remember when you're selling premium for range bound type strategies like iron condor strangle straddles butterflies calendars You've got to protect yourself to the downside because the velocity of the moves to the downside Happened quicker than they do to the upside now puts a little bit of a drag on our portfolio Performance when the market's just rallying Seemingly seemingly every day We finally got some downside. Hopefully that's a sign of more two-sided action in the market So want to keep that downside protection on because it will benefit us in the long run Next trade was a closing trade in ADSK. So this was a pre earnings long call That we had put on last week And so we were we were just looking for an expansion in implied volatility as well as a Up move in price got both of those. There's an excellent trade Book that in less than a week for a nice profit there And actually let's go to our trade Closed trades to take a look at what we got there may 232 bucks on that So nice nice profit there The xlv butterfly that I mentioned before that we closed Booked a hundred bucks on that so Closed trades are have been awesome this month. So we continue to book really nice winners in november Let's go back to our alerts And continue where we left off here. So that was the ADSK Next trade was a closing trade where we bought back a strangle in xrt So booked that for just under 50% of max profit. So that was a nice trade in xrt Now xrt I was actually looking to re-enter this today in in the january cycle But it just was not getting the amount of credit To make that I thought was those that was worth it. So applied volatility has come down significantly the ivy percentile is still over that 50 mark So if we get a pop back up, we made a look to enter xrt a new a new position next week So we'll continue to monitor that for now. We're we're completely out of xrt Next trade was a an opening trade that we put on today And that was a post earnings short foot. So this was in footlocker Which footlocker is not a stock that we trade a lot But we really like these high probability post earning trades where if if the price Opens up above the expected move For the for the earnings announcement Then it's typically going to stay higher and and maybe grind or go higher In on kind of a continuation basis. And so that's what we were looking for in footlocker So as soon as the market opened we started trying to get filled and you could do a you could sell short We did short naked puts in this case Because it's only a it's a lower price stock footlocker is a $40 stock We wanted we we used short naked puts you could have used Short put verticals, but I just didn't see and see getting enough credit because of the low price of this stock So yeah, we got in when price had moved down to about here. So we're up a little bit on this trade Up about 70 bucks. Uh, I want to take at least 100 150 200 bucks out of this trade So hopefully in the next week we see a little bit of a continuation higher in footlocker next trade And uh, this was also today we opened a new trade in E w w which is the mexican etf This is our first position that we entered in the january cycle. It's got 63 days to expiration Looking for a 30 to 50 percent of max profit To book a winner in E w w. So as you can see we just put it on today So still pretty centered in our range E w w the ivy percent I want to put this on was at 60 So we've already got a little bit of a contraction there So we'll continue to monitor E w w And lastly last trade was an opening adjusting trade in Baidu So this is one that we had on originally an iron condor earnings play And it made a pretty decent move to the downside. So we adjusted that so let's go to the platform And I'll show you what we've got on in Baidu now We've got uh, that's not what we want Baidu So we're going to analyze tab. This is the the alert that I was just talking about We put on a new iron condor as an adjustment here And then we're also holding our short put vertical Here so we want price to stay kind of higher to Range bound and and hopefully we can benefit on both of these pieces And in book a profit in Baidu So you can see after earnings we had that decent move down and then prices just kind of stayed fairly range bound here So continue to monitor Baidu This was a good one put on ivy percentile over that 50 mark So just collect more credit extending our duration giving ourselves more time to be right Let's take a look at some of the other positions still got a knack gas iron condor on And you can see price is still well within our range here not enough profit to take off if we look at the Uh, corresponding etf ung you can see implied volatilities continue to stay high one of the higher Higher ones on the board at this time. So continue to watch ung Soybeans we've got an iron condor on in soybeans Still well within our range here just trying to work our way back to profit in soybeans In wheat we've got two different positions on we've got our iron condor here Not enough profit to take off yet, but very centered and then we've got our Short put vertical price kind of when we originally adjusted this price broke down Through our break even now prices moved back up nicely just need a little bit more move up We've only got a few days left seven days left to expiration. So we'll be getting out of this piece Regardless of what price does but looking for a little bit of a price move higher To hopefully book a book a small profit out of that piece the iron condor I mentioned adobe So we've got this full iron condor on in adobe And we've also got the Short call spread So I need a little bit of a down move to range bound in adobe to benefit that piece I mentioned by-do di a we've this was uh previously an iron condor And then we've rolled this short call vertical to keep some of that short delt in our portfolio So just looking for a little bit more continuation to the downside to benefit that piece I mentioned eww EWZ made a nice move up today kind of back in the center Not quite enough to take off for profit here. I want to look at about 40 percent at least Of max profit before we book that one I mentioned footlocker fxi. We've got a strangle on here Price very centered here. Just waiting for some more profit to accumulate in fxi I mentioned idm iwm. We've got an iron condor on And uh just need a little bit more of a down move a little bit more time to pass to collect some more theta in iwm And the cues we've got this adjusted iron condor, which is so it's just the call side call vertical side Looking for a little bit more of a down move in in the cues to benefit that piece Then we've also got the full iron condor on Again could use some more downside and some more theta decay to benefit that Both of those are in december 28 days to expiration. So we got a lot of time left in those And then finally xop So xop came down almost tested our short strike almost came down to the point of needing to adjust And then his rally back nicely here. So well back within our range. We're down a little bit currently on the trade But just need a little bit more movement to the upside and a little bit more contraction In implied volatility to benefit xop So i hope that was helpful. If you guys have any questions, let me know. Otherwise. Have a great weekend. Talk to you next week


Hi everyone, Sean with jaspyscasebreaks.com here doing a jaspys nine box heisman mixer random team We're at the end. We're gonna be giving away a kyler murray rookie optic auto numbered out of 25 There's the auto Graded vgs 910 very nice Back to the randomizer 32 spots 32 teams We've got the list of customers From richard fox first spot purchased Bunch of fillers to give spots away as well Down to nix dober in the spot that he won in black three filler that we just did right before this And from the washington football team all the way back up to the arizona cardinals Dice roller to see how many times we're gonna randomize each list And it's a two and a three five times one two three four And fifth and final time on the team or the names from nix dober down to gary lazo And um, we will use a new dice roll for the giveaway at the end of the break as well Now we're gonna do five times on the teams and pair both lists up one two three four And fifth and final time from the las vegas raiders Down to the hueson texas okay, so nix dober with the raiders joshua zets with the panthers gary lazo with the packers tyler brender with the patriots nix dober with the chiefs gary lazo with the rams drew kerr with the giants arthur hackett with the seahawks tyler brender with the colts gary lazo with the ravens tice sasito drennan with the bangles Sean maddock with the dolphins christian woodland with her lions Sean maddock with the chargers richard fox the eagles patrick katzik with the cardinals ryan harrell with the falcons patrick katzik with the browns nix dober with the 49ers joshua zets with the redskins jarell taylor with the saints joshua zets with the bills erin pettit with the broncos christian woodland with the jaguars nix dober with the steelers joshua zets with the jets and the buccaneers Sean maddock with the cowboys erin pettit with the titans nix dober with the vikings and the bears And gary lazo with the texans i'm gonna go ahead and sort this by team name trade window open right now guys remember there are multiple years worth of product in here 2013 2019 and 2020 if there's no talk of trade we will go ahead and print and rip so while that's happening i'll go ahead and give it amount of time of opening our first box i'm getting the packs out 2013 certified it's gonna start it off this is our giveaway at the end i'll set that over here no talks of trade i understand only about 20 people in the chat right now so let's go ahead and just print and go you've got last year's rookie class current year rookie class and then back to 2013 good wide receiver rookie class tight end rookie class as well so here is the finalists no matter how you got into this break whether it was by purchasing a spot or by giveaway congratulations and thank you for getting this done no veteran based ship in this obviously all rookies hits and parallels do ship hopefully we see some really really nice hits in here football officially starts in eight days is it scary to say somehow we made it here with the way that uh baseball kind of had it's yeah one week now but um with the way baseball kind of stumbled to the finishing line not everywhere brad mark still uh still on uh wednesday um the way they finish this stumbling line like to the starting line i thought i for sure we would have something similar happen with football but so far not which is great talking about 2013 rookies levy on bell relic 54 out of 99 going to the pittsburgh steelers nicks stover last spot mojo kevin minter to 999 rookie for the arizona cardinals patrick katzik dj fluker rookie auto to 999 for the san diego chargers which is now the los angeles chargers shon maddock mike wallis to 250 miami dolphins going to shon maddock ryan nasib to 999 going to the new york giants and joker certified skills relic tory smith for the baltimore ravens gary lazow dj fluker rookie blue to 100 los angeles chargers shon maddock zavier rode to 999 for the minnesota vikings nicks dober jonathan franklin rookie patch auto for the green bay packers gary lazow that's number to 250 bow jackson to 250 for the los vegas radars nicks dober eric dickerson for the los angeles rams to 999 going to gary lazow that's 2013 certified 2013 community black next up daryl washington to 399 for the arizona cardinals patrick katzik charles tillman to 399 for the chicago bears nicks dober kenny vicaro rookie to 399 for the new orlean sainz jerel taylor bj daniels rookie to 399 for the san francisco 49ers nicks stover navarro bowman to 399 for the 49ers and nicks dober and zack sudfeld to 49 rookie for the new york jets joshua zets rookie patch auto ej manual to 99 buffalo bills and joshua zets we do have a redemption which would be expired at this point but hopefully panini can still either redeem that or redeem it for some points for you katalim sturges for the miami dolphins rookie to 199 miami dolphins going to shawn matik relic for the philadelphia eagles to 299 to shawn jackson eagles richard fox after all this time he is back with the eagles and another rookie relic landry jones to 299 for the pittsburgh steelers nicks dober and a plate for eddy lacy metal rookies rookie eddy lacy skinny eddy lacy still big eddy lacy i don't remember what he was at this point less than big massive eddy lacy green bay packers gary lazo and the redemption marcus latimore rookie signature materials prime marcus latimore would have been i believe a saint we'll double check this but i believe this is saints marcus latimore was a rookie with which if that's the case it would be jarell taylor uh let's start with playoff this is going to 2019 now so skipping forward to last year's draft class from 2013 to replay but that was crazy rockets thunder you know what brad i think you're right i was thinking uh who's the who's the safety or cornerback is it march on latimore marcus latimore is the running back from south carolina that blew up his knee in like last game this season or was it the bull game and was drafted by the 49ers i was really rooting for that guy he was incredible that's who we're looking at i'll double check it but marcus latimore if he's that i think that's who it is marcus latimore is probably the running back like he said i'm gonna look it up no matter what i think he's the running back that actually was drafted by the san francisco 49ers that redemption then in that case would go to nix dover um either way i'm going to be checking that obviously before it goes out so rookie auto for the new york giants julian love uh new york giants that's going to drew her nick bosa rookie wave san francisco 49ers nix dover lamar jackson first down to 99 for the baltimore ravens gary lazio rookie wave dk metcalf seattle seahawks arthur hackett travon williams first down to 99 for the sincennaday bangles tice arsado drennan rookie stallions relic for the washington football team terry maclauren going to joshua zetz darwin thompson rookie auto for the kansas city chiefs going to nix dover third down to 25 shan murphy bunting rookie for the tampon bay buccaneers joshua zetz russell wilson first down to 99 seattle seahawks that's going to arthur hackett erin donald stargazing rookie stallions relic two color 42 out of 50 for the new york giants that's going to drew her uh that was derrius leton and josh jacobs for the los vegas raiders nix dover let's do 2019 slash we've got a leaf metal break coming up later on for this year's draft class all of these will be checking uh with uh the checklist or college rules if there is no checklist so it would go to either team they're currently on if there is no checklist or team they played for the longest if they're no currently no longer on any team alex barnes emmanuel hall 13 out of 50 diante thompson 42 out of 50 we've got a redemption and that is david montgomery base auto obviously only been with the chicago bears don't see an expiration on there so still should be good most of somewhere missing but and drew lock there you go denver broncos flash of greatness drew lock auto 29 out of 50 denver broncos that's erin pettit congrats that uh david montgomery redemption that'd be going to the chicago bears nix dover and i'll double check i believe this diante thompson is that the i'll double check before we say anything more uh illusions 2019 it's not a bad option rex next uh wednesday right save the encased card for the end i agree have you seen any uh release as far as prospect names or anything that we should you know like the big prospect name that we're looking at rex it's obviously all the ones you know from the most recent draft and stuff are going to be in draft so nick and i were trying to see but i haven't i hadn't seen like a international prospect name or anything tagged to it to kind of earmark that spot you know yeah so it'll be a surprise for everybody i guess right let's do this one erin rogers mystique lavion bell the 299 for the new york jets joshua zetz it's a 299 kyler murray mystique very nice arizona cardinals patrick katzik and drew lock relic for the denver broncos going to erin pettit no numbering on that travis kelsie the 399 for the candid city chiefs going to nick stober ed reed the 399 for the baltimore ravens gary lazow nick bosa relic for the san francisco 49ers going to nick stober what's up smoky first impressions patch and auto nice three-color patch there 42 out of 99 michael hardman jr for the candid city chiefs nick stober hakeem butler 4 out of 50 arizona cardinals patrick katzik no schedules updated but it's definitely not done all update orders after the couple breaks that we have the neil hunter clear shots tonight to 399 minnesota vikings nick stober drew lock rookie denver broncos erin pettit cooper cup to 299 for the los angeles rams gary lazow russell wilson to 299 seattle seahawks arthur hackett diante thompson rookie auto the 199 for the arizona cardinals patrick katzik seacwon barkley to 399 for the new york giants and drew ker and are encased card for the washington football team rookie endorsement signature terry mcloren 61 out of 99 going to joshua's that's guys joshua scary terry he's pretty awesome drew haskins looks like he's been working on himself this year as well so hopefully that'll add up to both of them having a very big year got obsidian football here starting off with paris cambo rookie numbered to 125 for the indianapolis colts and tyler brender followed by atomic relic marlin mac also for the indianapolis colts going to tyler brender number 21 out of 50 quadri allison rookie auto for the atlanta falcons going to ryan herald numbered 65 out of 75 greedy williams rookie auto for the cleveland browns patrick katzik numbered 77 of 150 and nice rookie patch auto for nakeel harry and the new england patriots for tyler brender that is numbered 17 out of 50 see what he does with cam newton as a quarterback this year damien harris green 22 out of 25 also going to tyler brender and the new england patriots and tunnel vision green nice color matching there erin rogers for the green bay packers six out of 25 green bay packers gary lasso let's go elite now to current draft class 2020 so 2020 draft class looking for all those big time quarterbacks justin herbert joe burrow to atlanta jordan love jalen herz who is week one starters have they announced i'm assuming joe burrow obviously i don't think they've announced it but i'm assuming he's joe the week one starter obviously jordan love is not justin herbert is not they've announced tyron taylor uh tua is still not healthy all the way correct anybody um jalen herz obviously not carsten wenz unless carsten wenz i think did sit out because he's got a soft tissue injury i think that's just precautionary but i guess football does start in a little bit over a week or a week depending on the team thanks jason what is it do i do something different is it the beard it's probably the beard if that's the case that i understand kyler murray really nice relic there not rookie but primary colors look at the patch it's probably from the cardinals nal ds 15 out of 25 arizona cardinals patrick katzik rookie elitists speaking of joe burrow going to the cincinetti bangles tice sarsado randon and another joe burrow to 799 for the cincinetti bagels there you go tice ryan tanhill to 399 for the tennessee titans with his big contract erin pettit that is numbered patrick mahomes to 349 full throttle can city chiefs nicks dober well i appreciate that jake of eason from the indianapolis colts penn pals autograph tyler brenner he should uh do pretty well learning under my one of my favorites of all time philip reverse jerry judy of 399 for the denver broncos erin pettit dj dallas to 799 seattle seahawks arthur hackett jordan elliot to 99 for the cleveland browns patrick katzik another joe burrow numbered to 399 cincinetti bangles tice so no in the background barry melrose just said kale macarre is going to win uh rookie of the year to anybody that was here during that uh football or the hockey break to michael thomas to 349 spellbound a little bit of chip there on the bottom for the new orlean saints uh jarell taylor unfortunately that i would like to say that that's uncommon but i've seen quite a few of those spell bounds that are like that and then rookie on deck to a toga vialoa for the miami dolphins shon mattiff brian edwards to 799 los vegas raiders nick stover devin singletary to 399 for the buffalo bills josh wasets nice robert smith 27 out of 99 elite auto minnesota vikings nick stover big fan of him erin donald to 49 for the los angeles rams and gary lazo and that was 2020 elite now let's move it up to 2020 certified and we'll finish off with gold standard certified just came out last week i think patrick queen potentially the new ray lewis to 299 baltimore ravens gary lazo Leonard fornet to 450 no longer with the jacksonville jaguars but currently on this card he is so that's going to christian woodland anthony miller relic to 299 for the chicago bears going to nick stover henry rugs the third 2020 going to the los vegas raiders and nick stover aj dylan patch and auto to 349 for the green bay packers gary lazo oh well in the ray rice allegedly thing uh brad but yeah patrick queen very good at football that thing all allegedly didn't murder he was i believe he withheld uh evidence didn't testify against a friend who may have stabbed somebody brad if i remember correctly 399 la michael perine for the new york jets joshua zetz vaughn miller to 199 for the denver broncos erin pettit berry sanders seven out of 10 the greatest easily one of the greatest detroit lions christian woodland cd lamb for the dallas cowboys 2020 going to shawmatic kj osbourne rookie auto for the minnesota vikings that's numbered to 199 minnesota vikings nick stover and relic for the new york football team non-numbered chase young going to joshua zetz and last box guys gold standard football and then we're going to do that giveaway for the kyla murray so stick around no matter how good or bad your break has been here's your chance to either make it that much better or completely redeem yourself joshua kelly one of one there you go los angeles chargers my team shawmatic guts and jimmy groplow seven out of 99 for the san francisco 49ers nick stover one of one in the break there you go and it's for my team garter minshew white gold relic to 199 jacksonville jaguars christian woodland that jimmy groplow is going to the 49ers nick stover dual relic seguin barkley daniel jones to 199 for the new york giants and drew ker max crossbee gold strike auto 56 out of 99 not a fan of the raiders but that guy definitely puts in some work uh that is going to los vegas raiders and nick stover rookie auto coal comet to 199 for the chicago bears also nick stover and how about potentially rookie of the year Clyde edwards helair 30 sorry 16 out of 99 rookie patch auto for the canda city chiefs and nick stover as well so let's go over to the randomizer that was the break guys nice break nine boxes quick pretty nice let's go back over to the randomizer where we're going to give away this kyler murray as you can see right here very nice card here is the original list of customers from richard fox down to dick stover and the spots he won in black three new dice roll and at the end name on top after six and a six twelve definitely going to make a sweat for this one guys after 12 times name on top wins this kyler murray rated rookie optic auto numbered out of 25 bgs 9 10 good luck everyone one one two three four five six seven eight nine 10 11 and 12th and final time you can see 12 times on the randomizer guys good luck to everybody from spots two down to 32 thank you very much for getting into this break and let me do this for you guys hope i did well for you during the break if not hopefully you give me a second chance i can do it again for you ryan herald shawmatic thank you guys congratulations to nick stover with the spot he bought straight up last spot mojo it is a real thing guys congratulations nick you just won yourself this kyler murray rated rookie auto numbered out of 25 the purple stars parallel rated a 9 10 bgs centering corners both the nine edges surface both nine and a half and you can see the numbering right there seven out of 25 very nice dude congrats rookie of the year right there right so guys that was jaspy's nine box heisman mixer random teams i'm shan jaspy's case breaks dot com we'll see you next time