caa a22 4500 605476292 CHVBK 20210128100355.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s10985-014-9301-0 doi (NATIONALLICENCE)springer-10.1007/s10985-014-9301-0 Quantifying the average of the time-varying hazard ratio via a class of transformations [Elektronische Daten] [Qingxia Chen, Donglin Zeng, Joseph Ibrahim, Ming-Hui Chen, Zhiying Pan, Xiaodong Xue] The hazard ratio derived from the Cox model is a commonly used summary statistic to quantify a treatment effect with a time-to-event outcome. The proportional hazards assumption of the Cox model, however, is frequently violated in practice and many alternative models have been proposed in the statistical literature. Unfortunately, the regression coefficients obtained from different models are often not directly comparable. To overcome this problem, we propose a family of weighted hazard ratio measures that are based on the marginal survival curves or marginal hazard functions, and can be estimated using readily available output from various modeling approaches. The proposed transformation family includes the transformations considered by Schemper et al. (Statist Med 28:2473-2489, 2009) as special cases. In addition, we propose a novel estimate of the weighted hazard ratio based on the maximum departure from the null hypothesis within the transformation family, and develop a Kolmogorov $$-$$ - Smirnov type of test statistic based on this estimate. Simulation studies show that when the hazard functions of two groups either converge or diverge, this new estimate yields a more powerful test than tests based on the individual transformations recommended in Schemper et al. (Statist Med 28:2473-2489, 2009), with a similar magnitude of power loss when the hazards cross. The proposed estimates and test statistics are applied to a colorectal cancer clinical trial. Springer Science+Business Media New York, 2014 Average hazard ratios nationallicence Crossing hazards nationallicence Non-proportional hazards nationallicence Survival analysis nationallicence Weighted estimation nationallicence Chen Qingxia Department of Biostatistics, Vanderbilt University, 37232, Nashville, TN, USA aut Zeng Donglin Department of Biostatistics, University of North Carolina, 27599, Chapel Hill, NC, USA aut Ibrahim Joseph Department of Biostatistics, University of North Carolina, 27599, Chapel Hill, NC, USA aut Chen Ming-Hui Department of Statistics, University of Connecticut, 215 Glenbrook Road, U-4120, 06269, Storrs, CT, USA aut Pan Zhiying Amgen Inc., One Amgen Center Drive, 91320, Thousand Oaks, CA, USA aut Xue Xiaodong Amgen Inc., One Amgen Center Drive, 91320, Thousand Oaks, CA, USA aut Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/2(2015-04-01), 259-279 1380-7870 21:2<259 2015 21 10985 https://doi.org/10.1007/s10985-014-9301-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10985-014-9301-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Qingxia Department of Biostatistics, Vanderbilt University, 37232, Nashville, TN, USA aut NATIONALLICENCE 700 1- Zeng Donglin Department of Biostatistics, University of North Carolina, 27599, Chapel Hill, NC, USA aut NATIONALLICENCE 700 1- Ibrahim Joseph Department of Biostatistics, University of North Carolina, 27599, Chapel Hill, NC, USA aut NATIONALLICENCE 700 1- Chen Ming-Hui Department of Statistics, University of Connecticut, 215 Glenbrook Road, U-4120, 06269, Storrs, CT, USA aut NATIONALLICENCE 700 1- Pan Zhiying Amgen Inc., One Amgen Center Drive, 91320, Thousand Oaks, CA, USA aut NATIONALLICENCE 700 1- Xue Xiaodong Amgen Inc., One Amgen Center Drive, 91320, Thousand Oaks, CA, USA aut NATIONALLICENCE 773 0- Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/2(2015-04-01), 259-279 1380-7870 21:2<259 2015 21 10985