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On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects

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  • Paul Frédéric Blanche

    (University of Copenhagen
    Copenhagen University Hospital–Herlev and Gentofte)

  • Anders Holt

    (Copenhagen University Hospital–Herlev and Gentofte)

  • Thomas Scheike

    (University of Copenhagen)

Abstract

Simple logistic regression can be adapted to deal with right-censoring by inverse probability of censoring weighting (IPCW). We here compare two such IPCW approaches, one based on weighting the outcome, the other based on weighting the estimating equations. We study the large sample properties of the two approaches and show that which of the two weighting methods is the most efficient depends on the censoring distribution. We show by theoretical computations that the methods can be surprisingly different in realistic settings. We further show how to use the two weighting approaches for logistic regression to estimate causal treatment effects, for both observational studies and randomized clinical trials (RCT). Several estimators for observational studies are compared and we present an application to registry data. We also revisit interesting robustness properties of logistic regression in the context of RCTs, with a particular focus on the IPCW weighting. We find that these robustness properties still hold when the censoring weights are correctly specified, but not necessarily otherwise.

Suggested Citation

  • Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    • Handle: RePEc:spr:lifeda:v:29:y:2023:i:2:d:10.1007_s10985-022-09564-6
    DOI: 10.1007/s10985-022-09564-6
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