The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring
Authors
Dong, GaohongMao, Lu
Huang, Bo
Gamalo-Siebers, Margaret
Wang, Jiuzhou
Yu, GuangLei
Hoaglin, David C.
UMass Chan Affiliations
Department of Population and Quantitative Health SciencesDocument Type
Journal ArticlePublication Date
2020-09-02Keywords
CensoringIPCW
hazard ratio
inverse-probability-of-censoring weighting
win probability
win proportion
win ratio
Biostatistics
Epidemiology
Metadata
Show full item recordAbstract
The win ratio method has received much attention in methodological research, ad hoc analyses, and designs of prospective studies. As the primary analysis, it supported the approval of tafamidis for the treatment of cardiomyopathy to reduce cardiovascular mortality and cardiovascular-related hospitalization. However, its dependence on censoring is a potential shortcoming. In this article, we propose the inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic (i.e., the IPCW-adjusted win ratio statistic) to overcome censoring issues. We consider independent censoring, common censoring across endpoints, and right censoring. We develop an asymptotic variance estimator for the logarithm of the IPCW-adjusted win ratio statistic and evaluate it via simulation. Our simulation studies show that, as the amount of censoring increases, the unadjusted win proportions may decrease greatly. Consequently, the bias of the unadjusted win ratio estimate may increase greatly, producing either an overestimate or an underestimate. We demonstrate theoretically and through simulation that the IPCW-adjusted win ratio statistic gives an unbiased estimate of treatment effect.Source
Dong G, Mao L, Huang B, Gamalo-Siebers M, Wang J, Yu G, Hoaglin DC. The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring. J Biopharm Stat. 2020 Sep 2;30(5):882-899. doi: 10.1080/10543406.2020.1757692. Epub 2020 Jun 17. PMID: 32552451; PMCID: PMC7538385. Link to article on publisher's site
DOI
10.1080/10543406.2020.1757692Permanent Link to this Item
http://hdl.handle.net/20.500.14038/46952PubMed ID
32552451Related Resources
ae974a485f413a2113503eed53cd6c53
10.1080/10543406.2020.1757692