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Activity Number: 134
Type: Contributed
Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #312289 View Presentation
Title: Generating Studies with Time-to-Event Outcomes and Correlated Time-Dependent Covariates
Author(s): Maria E. Montez-Rath*+ and Aya Mitani and Kristopher Kapphahn and Manisha Desai
Companies: Stanford University and Stanford University and Stanford University and Stanford University
Keywords: simulation ; correlated covariates ; time-to-event outcomes ; time-varying covariates ; longitudinal studies
Abstract:

Simulation studies are useful for evaluating and developing statistical methods for the analyses of complex problems. Generating relevant data for this purpose however can be complicated. Our study of the comparative impact of HIV protocols on the risk of cardiovascular disease -- involving the longitudinal assessment of HIV patients over time -- is such an example. The correlation structure across covariates and within subjects over time must be considered as well as the outcome of interest, time to myocardial infarction, which is right-censored. A challenge in simulating the covariates is to identify a joint distribution for variables of mixed type -- continuous, binary or polychotomous. An additional challenge is incorporating correlation over time within a patient where some variables may vary over time and others may remain static. To address these issues, we extend the work of Demirtas and Doganay (2012). Identifying a model from which to simulate the right-censored outcome that is a function of these covariates builds on work developed by Hendry (2013). In this paper, we describe a cohesive approach to generate data with time-varying covariates and right-censored outcomes.


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