Abstract
Individual subjects may experience recurrent events of same type over a relatively long period of time in a longitudinal study. Researchers are often interested in the distributional pattern of gaps between the successive recurrent events and their association with certain concomitant covariates as well. In this article, their probability structure is investigated in presence of censoring. According to the identified structure, we introduce the proportional reverse-time hazards models that allow arbitrary baseline function for every individual in the study, when the time-dependent covariates effect is of main interest. Appropriate inference procedures are proposed and studied to estimate the parameters of interest in the models. The proposed methodology is demonstrated with the Monte-Carlo simulations and applied to a well-known Denmark schizophrenia cohort study data set.
Disciplines
Longitudinal Data Analysis and Time Series | Statistical Models | Survival Analysis
Suggested Citation
Chen, Ying Qing; Wang, Mei-Cheng; and Huang, Yijian, "Regression Analysis of Recurrent Gap Times with Time-Dependent Covariates" (January 2002). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 103.
https://biostats.bepress.com/ucbbiostat/paper103
Included in
Longitudinal Data Analysis and Time Series Commons, Statistical Models Commons, Survival Analysis Commons