Semiparametric Proportional Rates Regression for the Composite Endpoint of Recurrent and Terminal Events

• Activity Number: 416
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Lu Mao* and Danyu Lin
• Companies: and The University of North Carolina
• Keywords: Counting process ; Dependent censoring ; Intensity function ; Inverse probability of censoring weighting ; Mean function ; Survival analysis

Abstract

Recurrent event data are commonly encountered in clinical and epidemiological studies. A major complication arises when recurrent events are terminated by death. To assess the overall covariate effects on the two types of events, we define a composite endpoint as the cumulative number of recurrent and terminal events over time and propose a semiparametric proportional rates model which specifies that the (possibly time-varying) covariates have multiplicative effects on the rate function of the composite endpoint while leaving the form of the rate function and the dependence among recurrent and terminal events completely unspecified. We construct appropriate estimators for the regression parameters and the cumulative frequency function. We show that the estimators are consistent and asymptotically normal with variances that can be consistently estimated. Simulation studies demonstrate that the proposed methods perform well in realistic situations. An applications to a cancer clinical trial is provided.