Semiparametric regression models for recurrent and terminal event data Public Deposited

Downloadable Content

Download PDF
Last Modified
  • March 21, 2019
  • Wang, Xiaoyan
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Recurrent events are common in many clinical or observational studies. It is often of interest to evaluate the effects of risk factors on the frequencies of recurrent events. The recurrence of serious events are usually subject to censoring due to the death of a subject which is likely to be informative. In this dissertation, we first consider an accelerated failure time marginal rate model for the cumulative number of the recurrent events over time, while taking the terminal events into account. The marginal approach does not require specifying the dependence structure between the recurrent events and the terminal events, and the mean function incorporates the facts that subjects who die cannot experience any further recurrent events. We develop an estimating procedure for both the regression parameters and the mean function by applying the inverse probability of censoring weighting technique(Robins and Rotnitzky(1992)). The proposed estimators are consistent and asymptotically normal. We investigate the finite-sample properties of the proposed estimators through simulation studies and provide an application to recurrent hospitalization data taken from the Studies of Left Ventricular Dysfunction (SOLVD) Treatment Trial data. Second, we propose a proportional rate model for the recurrent event given the subjects are still alive. Again the dependence between the recurrent event process and the terminal event is unspecified. We consider two estimating procedures for the regression coefficients and the mean function of recurrent events. Asymptotic properties of the proposed estimators are derived. Simulation studies are conducted to assess the finite sample properties of the proposed estimators and show that they perform well under the sample sizes considered. The proposed method are illustrated to the SOLVD Prevention Trial data. Third, we deal with the problem of missing covariates under the proposed proportional rate model. Under the MCAR assumption, we obtain consistent and asymptotically normal estimators by modifying the estimating equation of the proportional rate model. Extensive simulation studies are conducted to evaluate the finite sample properties of the proposed estimators. We also compare the efficiency of our method with the complete-case analysis and the full data analysis. The proposed method is applied to the aforementioned SOLVD data.
Date of publication
Resource type
Rights statement
  • In Copyright
  • "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics, Gillings School of Global Public Health."
  • Zhou, Haibo
  • Cai, Jianwen
Degree granting institution
  • University of North Carolina at Chapel Hill
Place of publication
  • Chapel Hill, NC
  • Open access

This work has no parents.