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

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    Authors
    Dong, Gaohong
    Mao, Lu
    Huang, Bo
    Gamalo-Siebers, Margaret
    Wang, Jiuzhou
    Yu, GuangLei
    Hoaglin, David C.
    UMass Chan Affiliations
    Department of Population and Quantitative Health Sciences
    Document Type
    Journal Article
    Publication Date
    2020-09-02
    Keywords
    Censoring
    IPCW
    hazard ratio
    inverse-probability-of-censoring weighting
    win probability
    win proportion
    win ratio
    Biostatistics
    Epidemiology
    
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    Link to Full Text
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538385/
    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.
    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
    10.1080/10543406.2020.1757692
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/46952
    PubMed ID
    32552451
    Related Resources

    Link to Article in PubMed

    ae974a485f413a2113503eed53cd6c53
    10.1080/10543406.2020.1757692
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    Population and Quantitative Health Sciences Publications

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