Title

The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring

UMMS Affiliation

Department of Population and Quantitative Health Sciences

Publication Date

2020-09-02

Document Type

Article

Disciplines

Biostatistics | Epidemiology

Abstract

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.

Keywords

Censoring, IPCW, hazard ratio, inverse-probability-of-censoring weighting, win probability, win proportion, win ratio

DOI of Published Version

10.1080/10543406.2020.1757692

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

Journal/Book/Conference Title

Journal of biopharmaceutical statistics

PubMed ID

32552451

Related Resources

Link to Article in PubMed

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