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InformationTheoretic Approach for Two Binary EndpointsFor the Schizophrenia study, presented in Section 2.2.2 (see also Chapters 12 and 13 for a similar analysis in R and SAS, respectively), the two binary endpoints are defined as:
In R, using the Surrogate package, for a multitrial setting with two binary endpoints, the function FixedBinBintIT can be used to estimate both FIGURE 14.6 Schizophrenia Study. Analysis using the informationtheoretic approach for two binary endpoints. individuallevel and triallevel surrogacy measures. For the schizophrenia study, the function is called in the following way: Sur<FixedBinBinIT(Dataset=Schizo, Surr=Panss_Bin, True=CGI_Bin, Treat=Treat, Trial.ID=InvestId, Weighted=TRUE, Pat.ID=Id, Model="Reduced", Number.Bootstraps=500,Seed=1) In the Surrogate Shiny App, the following specifications should be used in the data loading screen in Figure 14.1: the true (CGI bin) and the surrogate endpoints (PANSS bin), the treatment variable (Treat), the unit for which will be calculated (InvestId), and the patientâ€™s identification number (Id). In the same screen, the tab Fixed effects information theory (BinaryBinary ) is selected in order to perform the analysis. The number of bootstrap samples (Number.Bootstraps=500) and the seed (Seed=1) are specified in the left panel in Figure 14.6. For the schizophrenia study, trial and individual level surrogacy measures are equal to Rh_{t} = 0.8213 (0.7469, 0.87864) and Rh = 0.3305 (0.2992, 0.3623), respectively. 
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