The effect of omitted covariates in marginal and partially conditional recurrent event analyses
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Date
2019-04-01
Authors
Zhong, Yujie
Cook, Richard J.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
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.
Description
This is the peer reviewed version of the following article: Yujie Zhong and Richard J. Cook, The effect of omitted covariates in marginal and partially conditional recurrent event analyses. Lifetime Data Analysis (2019), 25(2): 280-300 which has been published in final form at https://doi.org/10.1007/s10985-018-9430-y.
Keywords
asymptotic bias, confounding, marginal, partially conditional, rate function, recurrent events