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

Yujie Zhong () and Richard J. Cook ()
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Yujie Zhong: University of Cambridge
Richard J. Cook: University of Waterloo

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 2, No 5, 280-300

Abstract: 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.

Keywords: Asymptotic bias; Confounding; Marginal; Partially conditional; Rate function; Recurrent events (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10985-018-9430-y

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