Evidence map›Paper›PMID 39080523›Full record

ArticleBMC medical research methodology2024

Comparison between asymptotic and re-randomisation tests under non-proportional hazards in a randomised controlled trial using the minimisation method.

Ryusei Kimura, Shogo Nomura, Kengo Nagashima, Yasunori Sato

Abstract readComparative Study
In one paragraph

Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Ryusei KimuraBiostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, Tokyo, 160-8582, Japan. kimura.ryusei@keio.jp.
Shogo NomuraDepartment of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.
Kengo NagashimaBiostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, Tokyo, 160-8582, Japan.
Yasunori SatoBiostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, Tokyo, 160-8582, Japan.

Funding

AMED JP23lk1503008JST SPRING JPMJSP2123
6 · The paper itself

Abstract

backgroundPocock-Simon's minimisation method has been widely used to balance treatment assignments across prognostic factors in randomised controlled trials (RCTs). Previous studies focusing on the survival outcomes have demonstrated that the conservativeness of asymptotic tests without adjusting for stratification factors, as well as the inflated type I error rate of adjusted asymptotic tests conducted in a small sample of patients, can be relaxed using re-randomisation tests. Although several RCTs using minimisation have suggested the presence of non-proportional hazards (non-PH) effects, the application of re-randomisation tests has been limited to the log-rank test and Cox PH models, which may result in diminished statistical power when confronted with non-PH scenarios. To address this issue, we proposed two re-randomisation tests based on a maximum combination of weighted log-rank tests (MaxCombo test) and the difference in restricted mean survival time (dRMST) up to a fixed time point

methodsWe compared the performance of asymptotic and re-randomisation tests using the MaxCombo test, dRMST, log-rank test, and Cox PH models, assuming various non-PH situations for RCTs using minimisation, with total sample sizes of 50, 100, and 500 at a 1:1 allocation ratio. We mainly considered null, and alternative scenarios featuring delayed, crossing, and diminishing treatment effects.

resultsAcross all examined null scenarios, re-randomisation tests maintained the type I error rates at the nominal level. Conversely, unadjusted asymptotic tests indicated excessive conservatism, while adjusted asymptotic tests in both the Cox PH models and dRMST indicated inflated type I error rates for total sample sizes of 50. The stratified MaxCombo-based re-randomisation test consistently exhibited robust power across all examined scenarios.

conclusionsThe re-randomisation test is a useful alternative in non-PH situations for RCTs with minimisation using the stratified MaxCombo test, suggesting its robust power in various scenarios.

Indexed as

Proportional Hazards ModelsRandomized Controlled Trials as TopicData Interpretation, StatisticalHumansModels, StatisticalResearch DesignSurvival AnalysisMaxCombo testMinimisationNon-proportional hazardsRe-randomisation testRestricted mean survival timeSurvival analysisWeighted log-rank test

Identifiers

PMID39080523
PMCPMC11290221

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