Early stopping by using stochastic curtailment in a three-arm sequential trial

Leung, Denis Heng-Yan, Wang, You-Gan, & Amar, David (2003) Early stopping by using stochastic curtailment in a three-arm sequential trial. Journal of the Royal Statistical Society: Series C (Applied Statistics), 52(2), pp. 139-152.

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Abstract

Summary. Interim analysis is important in a large clinical trial for ethical and cost considerations. Sometimes, an interim analysis needs to be performed at an earlier than planned time point. In that case, methods using stochastic curtailment are useful in examining the data for early stopping while controlling the inflation of type I and type II errors. We consider a three-arm randomized study of treatments to reduce perioperative blood loss following major surgery. Owing to slow accrual, an unplanned interim analysis was required by the study team to determine whether the study should be continued. We distinguish two different cases: when all treatments are under direct comparison and when one of the treatments is a control. We used simulations to study the operating characteristics of five different stochastic curtailment methods. We also considered the influence of timing of the interim analyses on the type I error and power of the test. We found that the type I error and power between the different methods can be quite different. The analysis for the perioperative blood loss trial was carried out at approximately a quarter of the planned sample size. We found that there is little evidence that the active treatments are better than a placebo and recommended closure of the trial.

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ID Code: 90576
Item Type: Journal Article
Refereed: Yes
Keywords: bonferroni adjustment, conditional power, interim analysis, predictive, power, stochastic curtailment, stopping time, monitoring clinical-trials, comparing 3 treatments, conditional power, tests
DOI: 10.1111/1467-9876.00394
ISSN: 0035-9254
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2003 Royal Statistical Society
Deposited On: 19 Nov 2015 04:56
Last Modified: 19 Nov 2015 04:56

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