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

Author

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  • Yujie Zhong

    (University of Cambridge)

  • Richard J. Cook

    (University of Waterloo)

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.

Suggested Citation

  • Yujie Zhong & Richard J. Cook, 2019. "The effect of omitted covariates in marginal and partially conditional recurrent event analyses," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 280-300, April.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:2:d:10.1007_s10985-018-9430-y
    DOI: 10.1007/s10985-018-9430-y
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    References listed on IDEAS

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    1. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    2. Cook, Richard J. & Lawless, Jerald F. & Lakhal-Chaieb, Lajmi & Lee, Ker-Ai, 2009. "Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 60-75.
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