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General independent censoring in event‐driven trials with staggered entry

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  • Jasmin Rühl
  • Jan Beyersmann
  • Sarah Friedrich

Abstract

Randomized clinical trials with time‐to‐event endpoints are frequently stopped after a prespecified number of events has been observed. This practice leads to dependent data and nonrandom censoring, which can in general not be solved by conditioning on the underlying baseline information. In case of staggered study entry, matters are complicated substantially. The present paper demonstrates that the study design at hand entails general independent censoring in the counting process sense, provided that the analysis is based on study time information only. To illustrate that the filtrations must not use abundant information, we simulated data of event‐driven trials and evaluated them by means of Cox regression models with covariates for the calendar times. The Breslow curves of the cumulative baseline hazard showed considerable deviations, which implies that the analysis is disturbed by conditioning on the calendar time variables. A second simulation study further revealed that Efron's classical bootstrap, unlike the (martingale‐based) wild bootstrap, may lead to biased results in the given setting, as the assumption of random censoring is violated. This is exemplified by an analysis of data on immunotherapy in patients with advanced, previously treated nonsmall cell lung cancer.

Suggested Citation

  • Jasmin Rühl & Jan Beyersmann & Sarah Friedrich, 2023. "General independent censoring in event‐driven trials with staggered entry," Biometrics, The International Biometric Society, vol. 79(3), pages 1737-1748, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:1737-1748
    DOI: 10.1111/biom.13710
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    References listed on IDEAS

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    1. Jan Beyersmann & Susanna Di Termini & Markus Pauly, 2013. "Weak Convergence of the Wild Bootstrap for the Aalen–Johansen Estimator of the Cumulative Incidence Function of a Competing Risk," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 387-402, September.
    2. Morten Overgaard & Stefan Nygaard Hansen, 2021. "On the assumption of independent right censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1234-1255, December.
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