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Adjusting win statistics for dependent censoring

Dong, Gaohong, Huang, Bo, Wang, Duolao ORCID: https://orcid.org/0000-0003-2788-2464, Verbeeck, Johan, Wang, Jiuzhou and Hoaglin, David C. (2021) 'Adjusting win statistics for dependent censoring'. Pharmaceutical Statistics, Vol 20, Issue 3, pp. 440-450.

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Abstract

For composite outcomes whose components can be prioritized on clinical importance, the win ratio, the net benefit and the win odds apply that order in comparing patients pairwise to produce wins and subsequently win proportions. Because these three statistics are derived using the same win proportions and they test the same hypothesis of equal win probabilities in the two treatment groups, we refer to them as win statistics. These methods, particularly the win ratio and the net benefit, have received increasing attention in methodological research and in design and analysis of clinical trials. For time‐to‐event outcomes, however, censoring may introduce bias. Previous work has shown that inverse‐probability‐of‐censoring weighting (IPCW) can correct the win ratio for bias from independent censoring. The present article uses the IPCW approach to adjust win statistics for dependent censoring that can be predicted by baseline covariates and/or time‐dependent covariates (producing the CovIPCW‐adjusted win statistics). Theoretically and with examples and simulations, we show that the CovIPCW‐adjusted win statistics are unbiased estimators of treatment effect in the presence of dependent censoring.

Item Type: Article
Subjects: WA Public Health > Statistics. Surveys > WA 900 Public health statistics
WB Practice of Medicine > WB 102 Clinical medicine
Faculty: Department: Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI): https://doi.org/10.1002/pst.2086
Related URLs:
Depositing User: Rachael O'Donoghue
Date Deposited: 02 Dec 2020 13:50
Last Modified: 07 May 2021 13:47
URI: https://archive.lstmed.ac.uk/id/eprint/16257

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