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Simulation studies are useful for evaluating and developing statisticalmethods for the analyses of complex problems. Performance of methods may beaffected by multiple complexities present in real scenarios. Generatingsufficiently realistic data for this purpose, however, can be challenging. Ourstudy of the comparative effectiveness of HIV protocols on the risk ofcardiovascular disease -- involving the longitudinal assessment of HIV patients-- is such an example. The correlation structure across covariates and withinsubjects over time must be considered as well as right-censoring of the outcomeof interest, time to myocardial infarction. A challenge in simulating thecovariates is to incorporate a joint distribution for variables of mixed type-- continuous, binary or polytomous. An additional challenge is incorporatingwithin-subject correlation where some variables may vary over time and othersmay remain static. To address these issues, we extend the work of Demirtas andDoganay (2012). Identifying a model from which to simulate the right-censoredoutcome as a function of these covariates builds on work developed by Sylvestreand Abrahamowicz (2007). In this paper, we describe a cohesive anduser-friendly approach accompanied by R code to simulate comparativeeffectiveness studies with right-censored outcomes that are functions oftime-varying covariates.

Simulating realistically complex comparative effectiveness studies with time-varying covariates and right-censored outcomes