Adjusting win statistics for dependent censoring
Department of Population and Quantitative Health Sciences
Statistics and Probability
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.
IPCW, Informative censoring, net benefit, win odds, win ratio
DOI of Published Version
Dong G, Huang B, Wang D, Verbeeck J, Wang J, Hoaglin DC. Adjusting win statistics for dependent censoring. Pharm Stat. 2020 Nov 28. doi: 10.1002/pst.2086. Epub ahead of print. PMID: 33247544. Link to article on publisher's site
Dong G, Huang B, Wang D, Verbeeck J, Wang J, Hoaglin DC. (2020). Adjusting win statistics for dependent censoring. Population and Quantitative Health Sciences Publications. https://doi.org/10.1002/pst.2086. Retrieved from https://escholarship.umassmed.edu/qhs_pp/1386