Robertson, David S. and Wason, James M. S. and Koenig, Franz and Posch, Martin and Jaki, Thomas (2023) Online error rate control for platform trials. STATISTICS IN MEDICINE, 42 (14). pp. 2475-2495. ISSN 0277-6715, 1097-0258
Full text not available from this repository. (Request a copy)Abstract
Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | PROSTATE-CANCER; MULTIARM; MULTISTAGE; multiple testing; online hypothesis testing; platform trial; type I error rate |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Computational Statistics (Prof. Dr. Thomas Jaki) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 16 Mar 2024 13:01 |
| Last Modified: | 16 Mar 2024 13:01 |
| URI: | https://pred.uni-regensburg.de/id/eprint/60193 |
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