The Safe Logrank Test: Error Control under Optional Stopping, Continuation and Prior Misspecification

Peter Grünwald, Alexander Ly, Muriel Perez-Ortiz, Judith Ter Schure
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:107-117, 2021.

Abstract

We introduce the safe logrank test, a version of the logrank test that can retain type-I error guarantees under optional stopping and continuation. It allows for effortless combination of data from different trials on different sub-populations while keeping type-I error guarantees and can be extended to define always-valid confidence intervals. Prior knowledge can be accounted for via prior distributions on the hazard ratio in the alternative, but even under ‘bad’ priors Type I error bounds are guaranteed. The test is an instance of the recently developed martingale tests based on e-values. Initial experiments show that the safe logrank test performs well in terms of the maximal and the expected amount of events needed to obtain a desired power.

Cite this Paper

BibTeX
@InProceedings{pmlr-v146-grunwald21a, title = {The Safe Logrank Test: Error Control under Optional Stopping, Continuation and Prior Misspecification}, author = {Gr{\"u}nwald, Peter and Ly, Alexander and Perez-Ortiz, Muriel and Schure, Judith Ter}, booktitle = {Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021}, pages = {107--117}, year = {2021}, editor = {Greiner, Russell and Kumar, Neeraj and Gerds, Thomas Alexander and van der Schaar, Mihaela}, volume = {146}, series = {Proceedings of Machine Learning Research}, month = {22--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v146/grunwald21a/grunwald21a.pdf}, url = {https://proceedings.mlr.press/v146/grunwald21a.html}, abstract = {We introduce the safe logrank test, a version of the logrank test that can retain type-I error guarantees under optional stopping and continuation. It allows for effortless combination of data from different trials on different sub-populations while keeping type-I error guarantees and can be extended to define always-valid confidence intervals. Prior knowledge can be accounted for via prior distributions on the hazard ratio in the alternative, but even under ‘bad’ priors Type I error bounds are guaranteed. The test is an instance of the recently developed martingale tests based on e-values. Initial experiments show that the safe logrank test performs well in terms of the maximal and the expected amount of events needed to obtain a desired power.} }
Endnote
%0 Conference Paper %T The Safe Logrank Test: Error Control under Optional Stopping, Continuation and Prior Misspecification %A Peter Grünwald %A Alexander Ly %A Muriel Perez-Ortiz %A Judith Ter Schure %B Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021 %C Proceedings of Machine Learning Research %D 2021 %E Russell Greiner %E Neeraj Kumar %E Thomas Alexander Gerds %E Mihaela van der Schaar %F pmlr-v146-grunwald21a %I PMLR %P 107--117 %U https://proceedings.mlr.press/v146/grunwald21a.html %V 146 %X We introduce the safe logrank test, a version of the logrank test that can retain type-I error guarantees under optional stopping and continuation. It allows for effortless combination of data from different trials on different sub-populations while keeping type-I error guarantees and can be extended to define always-valid confidence intervals. Prior knowledge can be accounted for via prior distributions on the hazard ratio in the alternative, but even under ‘bad’ priors Type I error bounds are guaranteed. The test is an instance of the recently developed martingale tests based on e-values. Initial experiments show that the safe logrank test performs well in terms of the maximal and the expected amount of events needed to obtain a desired power.
APA
Grünwald, P., Ly, A., Perez-Ortiz, M. & Schure, J.T.. (2021). The Safe Logrank Test: Error Control under Optional Stopping, Continuation and Prior Misspecification. Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, in Proceedings of Machine Learning Research 146:107-117 Available from https://proceedings.mlr.press/v146/grunwald21a.html.

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