论文标题
在随机临床试验中使用5步分层测试和合并常规的生存分析
Survival Analysis Using a 5-Step Stratified Testing and Amalgamation Routine in Randomized Clinical Trials
论文作者
论文摘要
随机临床试验通常旨在评估测试治疗是否相对于对照治疗延长了生存率。患者的异质性增加虽然值得概括性的结果，但可以削弱常见统计方法检测治疗差异的能力，从而有可能妨碍监管机构对安全有效疗法的批准。提出了一个新的解决方案。在分析计划中预先指定了在两种处理下具有预后生存的潜力的基线协变量列表。在分析阶段，使用所有观察到的生存时间，但对患者水平的治疗分配视而不见，“噪声”协变量通过弹性净COX回归去除。有条件的推理树算法使用缩短的协变量列表将异质试验人群分为预后同质患者的亚群（风险层）。在没有闪烁的患者水平治疗后，将在每个形成的风险层次层进行治疗比较，并将层级结果组合起来以进行总体统计推断。我们提出的5步分层测试和合并常规（5-Star）的令人印象深刻的功率性能相对于logrank测试的功率和其他常见方法，这些方法不利于固有结构化的患者异质性，并使用假设和两个真实数据集以及模拟结果进行了说明。此外，报告层级比较治疗效果的重要性（加速失效时间模型的时间比与模型平均拟合，并且根据需要，并且根据需要，COX比例危害模型拟合的危险比是个性化药物的潜在支持者。可以在https://github.com/rmarceauwest/fivestar上找到Fivestar R软件包。
Randomized clinical trials are often designed to assess whether a test treatment prolongs survival relative to a control treatment. Increased patient heterogeneity, while desirable for generalizability of results, can weaken the ability of common statistical approaches to detect treatment differences, potentially hampering the regulatory approval of safe and efficacious therapies. A novel solution to this problem is proposed. A list of baseline covariates that have the potential to be prognostic for survival under either treatment is pre-specified in the analysis plan. At the analysis stage, using all observed survival times but blinded to patient-level treatment assignment, 'noise' covariates are removed with elastic net Cox regression. The shortened covariate list is used by a conditional inference tree algorithm to segment the heterogeneous trial population into subpopulations of prognostically homogeneous patients (risk strata). After patient-level treatment unblinding, a treatment comparison is done within each formed risk stratum and stratum-level results are combined for overall statistical inference. The impressive power-boosting performance of our proposed 5-step stratified testing and amalgamation routine (5-STAR), relative to that of the logrank test and other common approaches that do not leverage inherently structured patient heterogeneity, is illustrated using a hypothetical and two real datasets along with simulation results. Furthermore, the importance of reporting stratum-level comparative treatment effects (time ratios from accelerated failure time model fits in conjunction with model averaging and, as needed, hazard ratios from Cox proportional hazard model fits) is highlighted as a potential enabler of personalized medicine. A fiveSTAR R package is available at https://github.com/rmarceauwest/fiveSTAR.
