The effect of omitted covariates in marginal and partially conditional recurrent event analyses.

ABSTRACT: There have been many advances in statistical methodology for the analysis of recurrent event data in recent years. Multiplicative semiparametric rate-based models are widely used in clinical trials, as are more general partially conditional rate-based models involving event-based stratification. The partially conditional model provides protection against extra-Poisson variation as well as event-dependent censoring, but conditioning on outcomes post-randomization can induce confounding and compromise causal inference. The purpose of this article is to examine the consequences of model misspecification in semiparametric marginal and partially conditional rate-based analysis through omission of prognostic variables. We do so using estimating function theory and empirical studies.

SUBMITTER: Zhong Y 

PROVIDER: S-EPMC6423006 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature