统计
事件(粒子物理)
计量经济学
计算机科学
数学
医学
量子力学
物理
作者
Ray Lin,Ji Lin,Satrajit Roychoudhury,Keaven M. Anderson,Tianle Hu,Bo Huang,Larry Leon,Jason J. Z. Liao,Rong Liu,Xiaodong Luo,Pralay Mukhopadhyay,Rui Qin,Kay Tatsuoka,Xuejing Wang,Yan Wang,Jian Zhu,Tai‐Tsang Chen,Renee Iacona
标识
DOI:10.1080/19466315.2019.1697738
摘要
The log-rank test is most powerful under proportional hazards (PH). In practice, non-PH patterns are often observed in clinical trials, such as in immuno-oncology; therefore, alternative methods are needed to restore the efficiency of statistical testing. Three categories of testing methods were evaluated, including weighted log-rank tests, Kaplan–Meier curve-based tests (including weighted Kaplan–Meier and restricted mean survival time), and combination tests (including Breslow test, Lee's combo test, and MaxCombo test). Nine scenarios representing the PH and various non-PH patterns were simulated. The power, Type I error, and effect estimate of each method were compared. In general, all tests control Type I error well. There is not a single most powerful test across all scenarios. In the absence of prior knowledge regarding the underlying or non-PH patterns, the MaxCombo test is relatively robust across patterns. Since the treatment effect changes over time under non-PH, the overall profile of the treatment effect may not be represented comprehensively based on a single measure. Thus, multiple measures of the treatment effect should be prespecified as sensitivity analyses to describe the totality of the data. Supplementary materials for this article are available online.
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