Hey guys, it's Nate, aka The Foot Accountant. Welcome back to the channel. It is Wednesday. That means this week's episode of Thursday Flipping. The Thursday Flipping guide for this week is what we need to cover today. Looking to add into the possible Thursday Flipping investments that we can make this week with the second week of the screen promo and with the first batch of screen cards coming back into packs sometime over this weekend. I wanna talk about that a little bit. I wanna talk about the possibility of people also saving their packs and not open them right away, but also talking about our general rewards, investing time period and maybe a secondary period as well depending on what happens with ultimate screen and with the timing of the panic selling and what kind of packs they put out and stuff like that. So I wanna talk about a few things in regards to timing of your sales this week. But again, Thursday Flipping, basically what it is is we buy cards Wednesday evening after 6 p.m. UK depending on if there's pack supply or not. Couple hours before rewards is a great time to be looking out on the market or like midnight UK time if you're up that late, especially it's easier here in the US because that's a great time zone and time period for us. But basically what we've been looking at in the past couple of weeks is we've been looking at the team of the week, what links, investments we could have to that. And then of course last week we looked at the screen cards and we were like, okay, people are gonna wanna buy cards that link with these screens. So this week what I've seen is some of those screen cards have stayed definitely a little bit higher after they have gone out of packs. The ones we talked about last week, Bernard I think is still somewhere around like 210, 205,000 coins somewhere in that range. He is gonna be going back into packs though in a couple of days. So that's a little bit of a risk that you might play if you wanna buy this card before rewards. When it comes to screen cards, any screen cards I'm gonna tell you guys to hold off on buying these until later in the weekend because the last couple of days of scream maybe with both teams in packs they throw out the first lightning rounds of the year. They're at least gonna throw out more promo packs. And I know that people are actually starting to talk about saving their week in league rewards for Thursday, Halloween day when the first team is supposed to go back into packs. EA has made it clear that the first set of screen cards with Bernard, Odomendi, Hamas Rodriguez, Zlatan and Delialli that set of screen cards is going to be re-entering the game on Halloween. Now they said Halloween through the rest of the event which is a little bit, you know it doesn't really tell us everything about that because does Halloween mean that it's gonna be on Thursday the actual day of Halloween? Does it mean they're gonna come back into packs Friday for the weekend? I think they're gonna be in packs Thursday until the end of the promotion which is Sunday at 6 p.m. U.K. based on when the second team goes out of packs. So the second team right here in packs four days, 14 hours that is Sunday at 6 p.m. U.K. when that ends. So I think the first set of screen team will come back into packs on Thursday and they will go out of packs at that same time. But stuff that we can buy this week, again this week's flipping period we're gonna be buying on Wednesday night and we're gonna be selling Thursday before 6 p.m. U.K. The big key here is to sell in that time where it's a very small window. This is not like very super easy investing that you can do to hold a card in your club for a while. This is a small, small window that you're gonna have to take advantage of buying Wednesday night before rewards and selling like six hours after rewards before 6 p.m. U.K. because that's when we could get more SBCs and more content that could drop some card prices on the market and that's why I wanna look at a second investing opportunity as well later this weekend. So if we look at these, this week's team of the week obviously isn't out when I'm recording this video so I won't spend a lot of time but we always look at the upcoming team of the week prediction that's gonna come out on Wednesday and some possible cards that could link to these cards that people would be getting read this week that could be decent investments. Henderson with his golf against Spurs, that's a possible informed card. I don't know if a lot of people would use that card. I'm gonna be totally honest with you because that card stats right there as it looks is definitely not as good as Ducore in Diddy or Wine Album with the 67 pace and just the all round stats are just okay on that card. I don't think you're gonna see as many people using that card so I don't wanna link investment that. Jamie Vardy is an interesting link investment for me, a Premier League striker informed. I expect that informed to be a decent amount of coins. I mean if you think of Marcus Rashford obviously he has the five star skills. But Rashford's card was 90,000 coins right away at the beginning of FIFA. I would expect this informed to be probably first day a little around 100K and then dipped down below that. At some point, obviously the Pulisic informed this week maybe an informed investment to that being Tammy Abraham, but that's not gonna make it into a lot of people's teams. This team in the week honestly does not look very good so when we're looking at investments this week unless Mbappe gets in which even if Mbappe does get in it's gonna be a very expensive card and you're not gonna really buy cards to link to him because not as many people, that's not gonna be a good week in the investment because not as many people can afford Mbappe or afford him in a team with other very meta meta cards. So the stuff that we like to focus on for week in the week flipping is stuff that is very viable and is very common and we see it in a lot of people's teams. And what do we see in a lot of people's teams? Well, we can go over here to flip in again and click on the most popular players in foot. This is a little page that we can click on. Drop down of players, go to popular. These are some of the most searched players on FIFA at the moment right now. And I like a lot of these guys. Obamian, I like him and I think he's high up here right now because he's got the link to that new screen Ozil card who I think a lot of people are going out of doing to get an untradable German center attack in mid. Ozil card, a lot of people like him. They like that card. And this would be the very easiest link to him at a center attack in mid. Fabinho, very high rated card. Again, I like this for a week in league flip. Messi's high rated. That's not really a week in league flip just because he is too high rated. Lucas is probably good. And then you have some special cards in here as well. Griezmann will be out of packs. Nyongolan will be out of packs. I really like this Nyongolan inform out of packs specifically because it is a position change.  But I think this card's gonna make it into a lot of people's teams. He's one of the best center mids in the Serie A. Great looking card. He always plays well in game and he has the hype. And there are definitely ways that you can link him as well. If you put him at right wing, you could link him to Mertens. You could link him to the new Insignate card. There are plenty of Serie I informs that you could link him to. So I like that card as an investment out of packs that people will move to a center mid position to change in game. Looking at some of these other cards just to start off with, I mentioned Fabinho looks good. Ducorre out of packs I think is going to do unbelievable. The French nationality this year is so key between La Liga and between the Premier League. You have Dignay, you have Ducorre, and all the guys using Umtiti, Laporte, Veron, Langley, left backs being Ferland Mendi, being Benjamin Mendi and Roussillian. The French nationality, especially around the back lines this year in FIFA is huge. Then Cisoco being a very popular card, Dembele being a popular card, Rashford and Bapé. So a lot of these cards that you see here on this list are going to be the ones that are very good for Thursday flips because they're very popular and they get used a lot. Obviously you have to be careful with some of these lower rated guys. Let's talk about Ferland Mendi. This guy's price is held up pretty well for the fact that he is an 80 rated card and we've had so many SBCs that continue to try to push this card's price down. And still he maintains a pretty solid price every week of around 40,000 coins.  Last week the market got very low on Wednesday. Look where he went from Wednesday into Friday. He went from 34,000 coins up to 44, he went up 10,000 coins for an 80 rated card that would have gotten packed a ton. So again, even though you think this card kind of like breaks all of what we know a low rated card should do during week and leave rewards, people aren't outgrowing this card. People aren't getting rid of this card because there really has yet to be a card that is better than him that surpasses him in terms of in-game playable value and the links that this guy gets to a lot of people in the game. This is the card that everybody's using at Leftback. So again, if you see him on Wednesday, let's say tonight they put out a pack SBC and you see this Freeline Mindy card dip down to like 38,000 coins, just under 40K. I see that as a perfect opportunity to try to buy in on this card under 40,000 coins on him, especially for like 38K. This could be a decent week and leave flip. It's a little scary to be honest still because the card is so low rated but that's a card that I think you could beat the supply on just because there's so much demand on a card like that. Looking at another Leftback in the Premier League, another French Leftback, not necessarily a Premier League Leftback. Another French Leftback, we're looking at Dignay's Informer, 85 rated. It is Premier League and that's the huge thing and this guy links to multiple players that I like going forward that are very popular in people's teams. That is any French guy, to be completely honest, first of all, but he also gets the Green Links too. Ducorre, who I think is very popular. You've seen his card price move a lot this week. That shows to me that he's a popular card and also Bernard. I think that Bernard is, you mean you saw how much he rose when he went out of packs last week for Weekend League Rewards. The man went from 170K after Weekend League Rewards. He was like 210, 215. So that's a huge rise on a card like that. So I could see this Dignay card again. I know we have another French Leftback in Theo Hernandez and a bunch of other gold card French Leftbacks in the game that people use but this one being from the Prem, being Everton and French. And having an 80 pace. It's a serviceable card. It's a usable card. If you see this guy back under 70,000 coins, let me take a look at his card specifically at where you would really wanna see this card at over on Footbin in terms of a price. It says he's 70K right now. He was 58K in packs and look at how much he's risen out of packs already. Obviously, today we maybe had a little bit of panic. Yeah, yesterday he was 64K today. Now he's all the way up at 70,000 coins. Monday he was down to 61K. So this card has risen a lot out of packs. Wait until we see what we get tomorrow but I just think this card could continue to rise even more, a little bit. Maybe even up to 80K. Is that stretching it for this card? Maybe a little bit? But I think it's very possible for the Premier League links and for the Bernard links that a lot of people are gonna use this card for. So if you see him get down to the middle 60s again, try to get some on bid, that's gonna be a great shout for an inform that's out of packs. And again, I mentioned this card already. I think Ducoray, as in a week in league investment, will be great as well. He's gonna be going out of packs. Yes, he is 84 rated. That means a lot of people have had the chance to pack his card over this week. And a lot of people have this card as a red but his demand seems to be unwavered. And this guy still has gone up from around 36,000 coins in midweek all the way up to 44K right now. And with the shadow chem style, you can honestly get around over 50K for this card on the market. Let me see if he's back over 50K with a shadow of 52K. Yeah, so he's around 50, 51,000 coins with a shadow. So when you're also trading for week in league flips, if you can find these cards at a low price with a shadow, with a hunter, with a very good chem style on them or a position change, that's something that can be a big time when people are buying their week in league teams because people will pay the extra buck for the chem styles. Kind of like what we do with chem style trading but with a week in league flip. So I like to do Corey out of packs as well. Let's move into some gold cards. To be honest, the gold cards that have moved every week are gonna move again this week. Clement Leng Le, literally the most popular center back in the game. I'm gonna say it. I think he's the most popular center back in the game. Him and Verran partnership in the back is the link to Ter Stegen, one of the most popular goalkeepers in the game and he's French. So he gets tons of links, 120K right now. Let's go back to flip in and check his price. I know that on Sunday night, this card was around 110,000 coins. You could pick him up then. 108, yeah, it looks like the past two weeks. He's gone 111 on Wednesday to 126. Last week, 108 to 124. This week, he was 110. He's now up to 120 already now but I could definitely see this card going back up to 130 even by the time we hit Thursday after rewards. So this is a card that's gonna go up again a lot on Thursday. So just watch this card today, Wednesday, as we do see possibly an SBC coming onto the game. Maybe EA puts out an SBC, some more promo packs or honestly just the sell off on the hype before we can make rewards on a Wednesday night. I like that Leng Le card. Now I wanna talk about a couple of attackers. Lacazette. Last week, I picked a couple of him up at rewards. I picked him up on Thursday night, I think for around like 75K, is where I picked him up last Thursday. And I gotta show you guys this graph because the graph might not do him justice but I was watching this card very closely last week. This card went flying last week.  I remember picking up a few of his cards. It says 77,000 coins but he was definitely lower than that. On Wednesday night when I was buying this card last week, I bought a few for 73, 74K. And I thought, yeah, he'd probably go to maybe like lower 80s because the week I made flips before that had just been okay. The man went all the way up to like 90,000 coins as you see right here. Friday, 90,000 coins is what this card went up to. So I think this card again has the great potential this week to be a great week in the investment. Basically if it's French, if it's above 85 rated, if it's a popular card that people are using, I'm for it. I'm literally for it. Fabinho, I like that card as well. He moves every week. Usman Dembele is like one of the most used cards in this game as well. I think he's somewhere around 40K. Somewhere around 40K. So this could be a flip as well. You maybe pick him up later in the evening. On Wednesday, 44K. Maybe you see him at around 40K flat. He could probably go to 50K this weekend as well. Another very popular card that I like. So yeah, that's kind of the stuff that I wanted to look at for Weekend League Rewards. Those are some good investments that I like personally. Scream cards, again, I'm gonna wait until the end of the weekend for those since they're gonna be in packs all weekend with SBC supply and stuff like that. And again, I wanna talk about people holding their Weekend League Rewards. I have talked to some people and a lot of people are under the impression. We got a sale, by the way. A lot of people are under the impression that they want to save their rewards for when both Scream teams are in packs. Because let's say if you open your rewards at, what is it like, 8AM UK time when they come out. 9AM UK when the footchamps and Weekend League Rewards are out and did rivals. Let's say that those new cards come into packs at 6PM UK. The Scream Team 1 comes back into packs. Let's just say it happens that way. You're gonna have a lot of people that will save up their packs until then. That's why I'm saying for you guys, I think there's gonna be a rise in the market between Weekend League Rewards release and when the content drops at 6PM UK. Then, if that's when the Scream Team from Set 1 is put back into packs, you might see another dip in the market as people who save their rewards go out and open them all at the same time. So you might see some influx of icons on the market. 59 minute snipes, some team of the week cards from the new team of the week. Of course, the Scream items that are now in packs and the gold cards. So it might be an opportunity to double dip this weekend with rewards. Depending on how many people save their rewards, if you start seeing a lot of stuff on Twitter, a lot of people start, I'm probably gonna pull out on Twitter, will you be saving your rewards? And I'll be asking people, hey, is this something you're gonna do so we can get a good idea of this? So check my Twitter for that. I'll put that out on Wednesday today. That's gonna give us a good idea of if that could happen. Again, if you start to see a lot of buildup and a lot of people talking about that, I would expect that to happen. But I still think a lot of people will just won't remember or they won't know or they'll just open up the rewards when they see them and be like, oh, sweet, rewards. So they'll open them up when the first Scream team is not in packs yet and we'll see that normal rise in the market. But again, a lot of the stuff that you wanna buy, like Lacazette, for example, if you see him back down here at the mid-70s, 75,000 coins, maybe even lower than that, that's a great buy for me. Again, the first sell time is going to be that early time before 6 p.m. U.K. on Thursday, the quick flips and stuff like that. But again, for the stuff that I think is gonna rise this week, keep it simple. The stuff that has risen in weeks past is gonna be the stuff that is gonna rise this week because there's literally not a lot new that happened on the game this week. There's not many new cards, not a great looking team of the week. French, Brazilian, Premier League, La Liga, Bundesliga, that's the stuff that has been rising, that's the stuff that has been in the hype and those are the popular players on this game at the moment. So stick to the basics, stick to what you know is good. If you have other questions about cards that you're thinking about looking at for Thursday flips, get on foot bin, check it out. Does the market show or does the graph show that their card moved last week? And again, make sure you're just careful with those cards that are on a lower value if they're a lower rated card, but still a high value. They're gonna get packed a lot to just watch that kind of situation with those cards as well. So that's what I think for Thursday flipping this week. Again, I think the same timeframe applies. Basically it takes a safe route, buy Wednesday night, sell Thursday before 6 p.m. UK, and then you're liquid and you're ready for whatever happens with the rest of the screen promo on the weekend. Get your weekly teams as well. If you wanna buy those, I would buy those before the weekend as well unless it contains a screen card and you maybe wanna wait later for that. But again, just number one, make sure to remind yourself to have fun. If you lose a few coins along the way, it's gonna be okay, all right? I'm just trying to help you guys understand the weekly shapes and shifts of the market and how to be the smartest with your coins in terms of making coins and buying your players for the right price. But if you enjoyed today's video, smash a thumbs up on it. If it helped you out a lot at all, I would greatly appreciate knowing that. Drop a comment down below if it did help you or if you have any other questions. And of course, subscribe to the channel if you are new. It's been Nate, The Foot Accountant. Catch you guys later. Peace out.


My name is Biniam Fakadu and I work as an HIT officer in the Auditium Memorial Hospital for the past 7 years. My name is Gamma Chismalkam. I am a Digital Health Lead Executive Officer in the Ministry of Health in Ethiopia. The HIT is a powerful platform using our health management system. We use it for decision making since 2017. We are using DHIS-2 in all facilities, in government facilities. Currently we have implemented DHIS-2 in more than 3,800 health centers in about 400 government hospitals, which is almost around 85% covered by DHIS-2. There are 37 units, also we call them data units or data points. We collect on paper beds. Once it is finished, we enter it into the DHIS using data entry form. Currently even we are using DHIS-2 for reporting as well as for tracking programs. Mainly we are using for HMIs, which is one of the large DHIS-2 we have. But besides that we are using for HIV, we are using for malaria program, for TB program and other programs as well. And additionally even we are using for the device management as well, we have recently started that. Before the DHIS-2 was implemented there is other old systems. They are a little bit messy or a little bit vast for us to collect and also to generate reports. But the DHIS-2 there is a couple of even maps, dashboards, pie charts, tables as you can see behind me. And also we share it for medical directors and COs. At different level the leadership has easily accessing the data of the facilities. And also at facility level also the healthcare providers, the physicians, other stakeholders are easily entering their data, getting the reports. We get feedback from all departments. There are NCOD, some cases that are not found in the DHIS. Which is also we feedback it to the ministry or the regionals below to include it in the next version or the next DHIS. They also see it and they make their own decisions based on the data. The system can easily present the data since it has a number of data presenting future and it's very easy for the end users. We expand our emergency departments based on the data. When we see the trend, there is also a point trend. So we just see also in the medical director hospital management decided we need bigger room, bigger beds. And this is one decision we come up from the DHIS using the data. We want to scale up the DHIS to entire the country. Specially we want to cover all private facilities to report through DHIS. And also we want to strengthen the team, the developers customizer teams locally. And even when to integrate other systems with DHIS we want to work more the interoperability area. And even want to follow the virgin upgrade of the DHIS. So we have a number of plans to increase or scale up the DHIS to system in the country. From our experience we have successfully implemented DHIS to the country. This is a good experience. And also our end users health facilities and all, they are very interested and are very happy with the system.


For our next speaker, we have Quinyun and communicating the robustness of the COVID-19 studies, and I will let her take it away. Hi, everyone. Today I will talk about work that has been an effort of a large group as listed here. We are from different universities and different disciplines, including social science, public policy, epidemiology, and public health. Briefly speaking, we are working on providing an approach to help better communicate the uncertainty or strength of evidence or the robustness of COVID-19 studies. The COVID-19 pandemic is forcing researchers and policymakers to accelerate the evaluation of treatments and vaccines. Given the urgency of the epidemic and the range of stakeholders involved, we really need a shared interpretation of the robustness of scientific findings. It's becoming more and more important than ever. Our goal is to provide such an approach so that we can have an intuitive assessment of research findings in terms that facilitate public health policy decisions. Why is this difficult? Well, one challenge is that, unlike those non-pharmaceutical interventions, we actually don't know what's going to really work here. There are a lot of research going on utilizing many different research designs, and particularly a lot of those research are of our small studies. And interpreting the robustness of small studies are not that straightforward. At the same time, just as some dashboards that are collecting information and results from different studies, it is not enough that these studies coming out. But also we need to synthesize all these studies that are coming out, and we need to make sense of them in almost a real time. So these are two big challenges we are having right now. So our approach is that we think inferences from all sorts of studies are actually always imperfect and uncertain. But we can always quantify the imperfection and uncertainty of your inference. To do this, we ask how large the bias needs to be to change your inference, or what would it take to change your inference? Dr. Ken Frank has been working on this for a very long time in a lot of different contexts. Together with the group, approaches have been developed for continuous outcomes and have been expanded to other models, including logistic regression and remediation. And also we have built Shiny R app, a bunch of different R functions, as I will show you later. And also we have data functions. So all of this has been already a pretty large effort, but it's not necessary related to COVID or medical trials. But when the COVID came out, we engaged with some other people who are epidemiologists or medical doctors. We are also who are also co-authors of this paper. And we thought we could actually apply the approach to the emerging COVID medical trials, and that could be useful. So I will start with an example, which is a first randomized trial of HCQ. This is an early study conducted at Renmin Hospital, Wuhan, China. 31 of 62 patients were randomly assigned to receive HCQ in addition to the standard treatment. And the inference they drew from this study is that HCQ is efficacious. The basis of their inference can be shown as two by two tables. They looked at several outcomes. One outcome is a reduction in pneumonia. This is a two by two table for this outcome. So we can see in this table in the control group, 17 out of 31 patients improved, while in the treatment group, that's this whole 25 out of 31 patients improved. So it is 81% versus 55%. And this is showing that the treatment has a significant positive effect. But we want to say more about the strengths of evidence here. We know this is statistically significant, but it is not a double-blinded trial, and this is a very small study. Can we better understand the robustness of the finding here? To do that, we start with a general approach our team has been working on. We call it a case replacement framework. Say you have a study where the estimated effect is six and you have a threshold of four. Then we can calculate that one-third, which is the black part, one-third of the estimated effect of six exceeds the threshold of four. That means one-third of the estimated effect would have to be due to bias to change your inference. Importantly, we can think about this one-third as one would need to replace one-third of the observed cases with zero effect cases to reduce the estimated effect of six below the threshold of four. So your inference would change. The key here lies on the replacement of observed cases with zero effect cases. This is why we call it a case replacement framework. So in this framework, the more evidence you have or the less uncertainty the study has, the more observed cases need to be replaced with zero effect cases. For example, say your estimated effect is not six but eight, then you would need to replace 50% of your cases to change your inference. And comparing 50% to one-third here, we know that study is more robust than this study. Notice that in this framework, the threshold can be pretty flexible. So you can apply this framework to various thresholds. The threshold can be based on substantive importance, such as a minimal level of clinical importance, or it can be based on statistical significance. For example, in a lot of scenarios, we are using the effect size where the study is no longer significant at 0.05 level. Now we apply this framework to dichotomous outcomes. Say we replace cases from treatment survival category. So we know that in 2x2 table, we have four cells. We start with replacing cases from one of the cells, which is treatment survival category. And we define the number of treatment success cases that would have to be replaced with zero effect cases to invalidate the inference as RIR, meaning robustness of inference to replacement. And then we use this RIR to quantify the uncertainty of the inference. Let's go back to the HCQ example and use this approach to quantify the robustness of inference that HCQ is efficacious. Then in our study experiment, we replaced three cases from the improved HCQ group. So we replaced three cases from this group with cases for whom HCQ has no effect. In other words, zero effect cases. But how can we know which cell the three replacement cases would go where they go to this cell or this cell? To answer this, we looked at the entire sample. Under the now hypothesis, HCQ has no effect. And then 20 out of 62 cases, which is around 32% cases, experienced, exacerbated or unchanged rather than improved. Applying this, we will know that 32% of the three replacement cases, which is approximately one case, would go to exacerbated or unchanged. And then the other two cases will stay here. So in the three replacement cases, one would go here and two would stay here. As a result, we would see one fewer case in the improved HCQ group and one more case in the exacerbated or unchanged group. From another perspective, we can also interpret this as one case switched from this group to this group. The switching approach is actually known as fragility in clinical epidemiology literature. Now, after the replacement or the switching, the inference changes. That is, HCQ is no longer efficacious anymore at the 5% level. Now in the treatment group, we have 24 out of 31 patients improved and in the control group, 17 out of 31 patients improved. And the p-value increases to 0.06. In other words, to invalidate the inference, you then need to replace three treatment success cases with now hypothesis cases, meaning RIR is equal to three. This is equivalent to transferring or switching one case from treatment success to treatment failure. As an extra note here, for those who are familiar with fragility, Walter et al. in one recent paper has illustrated that one switch can have very different meanings depending on how rare an outcome could be. And here the RIR framework complements fragility by calling for the rare, how rare an outcome could be or what percentage of cases would experience failure or success. In this example, I'm using the entire sample to estimate that and one can also use the control group to estimate that. In this figure, we extend the HCQ example by plotting RIR against corresponding estimated effect sizes along a continuum to represent a broader potential set of thresholds. Each data point here represents the RIR to reduce the estimated effect in the HCQ example below a particular effect size. So here we can see that consistent with our previous discussion, one would have to replace three of the observed treatment improved cases, with cases for which failure rate equals to 32 percent to reduce the estimate effect of 0.26 difference below the threshold of significance at 0.05 level. So it's from here to here. At the same time, this figure also shows that an RIR of about 16 to reduce the initial probability difference of 0.26 to 0.1. So from here to here, we would need to replace 16 cases. These 16 replacements would generate five switches, meaning fragility equals to five. And more generally, this figure represents the RIR with respect to any effect size, including effect sizes that define a minimal important difference. Now, as a second example, as evidence from multiple RCTs accumulates, adding the RIR to meta-analysis of RCTs can help assess and visualize the robustness of inferences beyond reporting or examining P values. To illustrate, we recreated as a study by study accumulation of 16 estimated effects presented in a meta-analysis of randomized trials examining the impact of anti-hypertensive treatments. In this figure, we present a series of robustness updates as each study was added in the hypertensive meta-analysis, where each subsequent point presents an updated estimate effect and the corresponding RIR was reported in each box here. Critically, the combined estimated treatment effect fluctuated by several percentage points until the A study, which was conducted in 1979. As studies progressed, the estimated treatment effect stabilized and the number of replacements it would take to invalidate the inference increased substantially. Continuous updates to an analogous figure using COVID-19 studies would present decision makers with an up-to-date and intuitive characterization of combined estimates as well as the robustness of the inference drawn from scientific evidence. To make this approach more accessible, we have made several functions in R to realize this, because this is still a work in progress, so I wouldn't need to install the development version as a code here. Then once the user installs the function and elaborates, they can call the function named as t-confund, and the four most important arguments for the function is for cells in the 2x2 table. The 14, 17, 6, 25 are the four numbers we saw before in the hcq 2x2 table. The function will return the user enter value that's our observed 2x2 table and the 2x2 table after replacement to change the conclusion. Also, in the function, the users can specify the p-values they want to focus on, or they want to use for the threshold, and whether they want to do the replacement or switch in the treatment role or in the control role, or what kind of tests they want to use, either high-square or for sure exact test, and whether they want to use the entire sample or the control role to estimate how rare the outcome could be. We also provide another function called t-confund and then underscore fake, and the user can use this function to reproduce figure that we have seen before for the hcq example. So in this figure, the RIR is plotted against corresponding estimated effect sizes, allow a continuum to represent a broader potential set of thresholds. Each data point represents the RIR to reduce the estimated effect below a particular effect size, and the arguments for this function are exactly the same as the arguments for the function before the t-confund function. In addition to the t-confund, in both our R-shiny and R-package, we also provide other functions to apply similar framework to conduct sensitivity analysis for a broader set of models, including models for continuous outcomes and logistic regressions for dichotomous outcomes. I listed three main functions here. The first one is t-confund, and the users can use this to conduct sensitivity analysis for published studies. So say you read a paper and you know the estimated effect standard number of observations and covariates, but you don't have access to the original data set, then you can use t-confund to conduct sensitivity analysis. You can use t-confund function to conduct analysis if you fit models in R, so you have the original data. Finally, you can use m-confund to conduct analysis for meta-analysis. To summarize, we introduced a case replacement framework for sensitivity analysis of clinical trials. The framework supports statements such as the inference would change if a certain number of the treatment patients who experienced a benefit were replaced by patients for whom there was no effect of the treatment. The framework also complements fragility by accounting for the rarity of negative outcomes, and also the framework can be used for any threshold, including the minimally important difference and statistical significance. Also, the framework applies to a broader set of models and research designs. Finally, back to our introduction, we hope that expressing uncertainty in terms of patient experiences make the robustness of inference clear even without deep knowledge of probability and statistics. We hope it could be especially helpful for small studies as interpreting the robustness of small studies can be tricky. And we also hope that the R&R can facilitate a common understanding among researchers, policymakers, journalists, clinicians, and the public about the strengths of evidence for potential new interventions. And this is an ongoing work, and we really look forward to your comments, suggestions, and questions. Feel free to send emails to any of us. Thanks. Thank you, Dr. Lin. I definitely wanted to join the chat group with their comments that this was an excellent talk. And thank you very much for your work and sharing that today. I really enjoyed that. Thank you. Let's see. Well, there is actually, there aren't any questions posted. And all right. So with that, we will go ahead and close this session and start the next. But once again, thank you very much for joining our conference and sharing.