 Today, I'm going to talk to you about the CRUS NMA. It's a new R package to synthesize the CRUS design evidence and the CRUS format data using Bayesian network meta-analysis. So standard network meta-analysis synthesized aggregate data from randomized clinical trials because it is easily accessible from the published literature. However, heterogeneity may be present across these trials and we include participant covariates or effect modifiers as aggregate information to explain some of this heterogeneity. Including being covariates could, however, induce aggregation bias. So ideally we would like to have or to include the covariates on the individual level from every study, but more typically we only have IBD from a subset of studies and then aggregated data from the rest. In terms of study design, most NMAs include only clinical trials in the analysis because they are typically at lower risk of bias, but on the other hand, they are conducted for restricted set of population and this makes the results hard to be generalized to the general population. On the other hand, an unrandomized studies or observational studies reflects better the reality but it comes with high risk of bias. We might need in some situation though to combine these types of evidence while taking into account the biases on each design. And this is what the CROUS NMA model is doing. It's a recent extension of the NMA model to synthesize mixture of data formats, IBD and aggregate data and different study designs. We built the CROUS NMA model by integrating these four different approaches which combine clinical and observational data into the three level hierarchical model that synthesize IBD and aggregate data. How does the three level hierarchical model work? First of all, we define an individual level regression model to include participant covariates. Here beta zero captures the diagnostic. Effect, beta W is the within study treatment covariate interaction and beta B quantifies the interaction between the relative treatment effect and the mean covariate value on study level. And then for aggregate studies, we have access only to mean covariates so we can only estimate the coefficient beta B. Then we combine the evidence across IBD and AD for relative treatment effect, delta and covariate coefficient beta B while beta W is combined only from IBD studies. Now, how do we integrate the four approaches to combine clinical and observational data into this three level hierarchical model? So the simplest approach is to not differentiate between the two types and fit individual regression model for both the clinical and observational studies and do the same for the aggregate studies. But we know that each type has different levels of bias and this model doesn't account for this bias. And this brings me to our second model which adjust the relative treatment effects to the bias on each study for individual model and aggregate model, we add this highlighted part, the bias effect of each study, gamma multiplied by the bias indicator of data study R. The bias indicator R takes values zero or one based on the assessment using the risk of bias tools. However, we know that the judgment using these tools is subjectives and carry many uncertainty and carry many uncertainty. And we reflect this uncertainty by assigning a Bernoulli distribution to the bias indicator which will provide zero or one to R. But now, and to estimate the probability of bias on each study, we either assign a beta distribution, low risk of bias studies are given, this distribution is q toward zero and high risk of bias studies q toward one. Or we could estimate the bias probability using study characteristics through this logistic model. There is another way to reflect study bias by directly modeling the bias adjusted relative treatment effect theta, which is estimated by a weighted average of unadjusted effect delta and the bias adjusted effect delta plus gamma. In bias adjustment model one in the slide before, this theta is either the unadjusted effect for low risk of bias studies or is a bias adjusted effect for high bias studies and not both of them. But here we allow theta to be weighted average of both parts but for low risk of bias studies we give greater weight to unadjusted and a little weight to bias adjusted part and bias versa for studies at high risk of bias. The last approach in combining clinical and observational data is using observational information as a prior to model the clinical evidence. It is a two-step approach. First step is to conduct network meta-analysis only for observational data using individual aggregated data or mixture of both. In Bayesian framework, we will get a posterior distribution of each relative treatment effect. Second step is to conduct network meta-analysis for clinical data but as a prior is we use the posteriors we get from the observational data. Now, some people could argue that usually observational data has a greater sample size compared to clinical trials and this makes the observational information dominate the estimate. So to control this potential high influence of observational studies, we download the contribution from these studies by increasing the variance from the posteriors here or make these distributions further. These models are implemented in the Kroosan-Amei package which is a suit of tools for performing network meta-analysis and meta-regression with individual participant data, aggregate data or mixes of both and each format can come from clinical trials or observational studies or mix of both. Behind the scenes models are estimated in a Bayesian framework using Jackson R the package allows for conducting the standard network meta-analysis when we have aggregate data from RCTs it allows the inclusion of IBD of these RCTs and it also enables each format include observational studies. So a workflow in Kroosan-Amei model is as follow. First of all, you start with your data sets, participant data and aggregate data or only a single data of one of them. There should be both of them in a long format which means one row bear R, bear aggregate study or bear participant in IBD. We then call the Kroosan-Amei model function to construct a JAX model and reformat the data. We might visualize the network and then we run the analysis using the Kroosan-Amei.run function and once the model is fitted we then kind of reduce summary of results, leak table or check the convergence. So let's apply this to an example. First of all, we have some individual participant data at the top here. So one row bear each individual and you can see that we have in this example a binary outcome. And then we have aggregate data below this. One row bear each other study with their outcome and covariates. On each data set you need to have at least these highlighted columns, study ID outcome, the assigned treatment and the design of the study clinical or observational. And for aggregate data you need additionally to provide the sample size column in. Combining these together in an Kroosan-Amei model. First of all, you need to indicate the name of your individual participant data and aggregate data. You need then to indicate columns name of these variables and you then choose which model to be used to combine treatment effects across studies either random or common effects model. You set the reference treatment. In this example, relative treatment effects will be evaluated versus A. And finally you indicate which approach you would use to combine observational and clinical data. Here goes one of these four approaches that I talked about before. To conduct network meta-analysis, a meta regression you could set a covariate. Here we use H to adjust all relative treatment effects. And also you could adjust the relative treatment effects to study bias using bias adjustment model one. And here there are too many arguments to be set as well. All will be described in the help file and the v-nit. You can also plot the network using the netplot function. You could pass to this function different arguments to control the colors, the thickness and many other features, which are very similar to the net graph function from net meta package. And then you can go away and fit the model using the cross-anime Dutron function. It takes the model we created using the cross-anime.model and you need to set the settings for MCMC samples, iteration burn-in and et cetera. Once we fitted the model, I can then print a summary of the results. So here it's showing us the mean, standard deviation, et cetera for each of the parameters. We have here the regression parameter B, the relative treatment effects D and the bias effect plus the two heterogeneity parameters. Also to provide the convergence information, the Gilman-Robini statistic and the number of effective sample size. You could also produce the trace plot to check convergence of each parameter. And finally you can create the leak table by cross-anime edit leak function. So here the values in each cell shows the relative treatment effects and the 95% credible interval of each treatment on top compared to the treatment on the left. So I really just gave a brief summary today if you would like to find out more and you can check out our package on GitHub website. Soon we will submit this package to CRAN with the Illustrator documentation and detailed example. We also submit our methods paper. Thank you very much.