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Abstract:
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The theory on valid inference about the treatment effect with time-to-event endpoints and covariate-adaptive randomization has been lacking until very recently. If the model is misspecified in trials with stratified randomization, the widely applied robust score test [Lin and Wei (1989); https://doi.org/10.1080/01621459.1989.10478874] was shown to be conservative by the groundbreaking work of Ye and Shao (2020; https://doi.org/10.1111/rssb.12392). This result, however, was not established for minimization other than via simulations. Interestingly, model misspecification caused by the omission of some factors from the analysis model is more common in trials when minimization is warranted. This talk will present recent work by Johnson, Gekhtman and Kuznetsova (draft manuscript, 2022) which extends the theory developed by Ye and Shao (2020) to show that both the log-rank and the robust score tests are conservative under minimization if the model is misspecified.
These results warrant the regulators' consideration to relax the requirements for re-randomization tests in addition to the score and log-rank tests. This talk will raise awareness about these developments.
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