A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies.
Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.
a Department of Statistics , Shahid Beheshti University , Tehran , Iran.
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M
Ganjali
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T
Baghfalaki
D000163:Acquired Immunodeficiency Syndrome / Q000175:diagnosis / Q000453:epidemiology* / Q000401:mortality / Q000628:therapy; D000465:Algorithms; D035843:Biomedical Research / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D018450:Disease Progression; D020276:HIV Long-Term Survivors / Q000706:statistics & numerical data*; D006801:Humans; D053208:Kaplan-Meier Estimate; D008137:Longitudinal Studies; D015233:Models, Statistical; D009010:Monte Carlo Method; D009716:Numerical Analysis, Computer-Assisted; D011379:Prognosis; D012107:Research Design / Q000706:statistics & numerical data*; D013997:Time Factors
Copula models; Expectation-maximization algorithm; Longitudinal model; Non-ignorability; Shared parameter model; Time to event model