Survival analysis using a 5‐step stratified testing and amalgamation routine (5‐STAR) in randomized clinical trials
Survival analysis using a 5‐step stratified testing and amalgamation routine (5‐STAR) in randomized clinical trials
复制标题
在随机临床试验中使用 5 步分层测试和合并例程 (5-STAR) 进行生存分析
DOI：
10.1002/sim.8750
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发表时间：
2020
影响因子：
2
通讯作者：
Rachel Marceau West
中科院分区：
文献类型：
--
作者：
D. Mehrotra;Rachel Marceau West
随机临床试验通常旨在评估测试治疗相对于对照治疗是否可以延长生存期。患者异质性的增加虽然有利于结果的普遍性，但可能会削弱常见统计方法检测治疗差异的能力，从而可能阻碍安全有效治疗的监管批准。针对这个问题提出了一种新颖的解决方案。分析计划中预先指定了在任一治疗下都有可能预测生存的基线协变量列表。在分析阶段，使用所有观察到的生存时间，但对患者级别的治疗分配不知情，通过弹性净 Cox 回归消除“噪音”协变量。条件推理树算法使用缩短的协变量列表将异质试验人群分割为预后同质患者的亚群（风险层）。在患者级别的治疗揭盲后，在每个形成的风险层中进行治疗比较，并将层级别的结果结合起来进行总体统计推断。相对于对数秩检验和其他不利用固有结构化患者异质性的常见方法，我们提出的 5 步分层测试和合并例程 (5-STAR) 具有令人印象深刻的功率提升性能，使用假设数据集和两个真实数据集以及模拟结果来说明。此外，报告分层水平比较治疗效果（来自加速失效时间模型的时间比率与模型平均的拟合，以及根据需要，来自 Cox 比例风险模型拟合的风险比）的重要性被强调为个性化医疗的潜在推动者。 R 包可从 https://github.com/rmarceauwest/ FiveSTAR 获取。
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. An R package is available at https://github.com/rmarceauwest/fiveSTAR.
影响因子：
1.9
作者：
PEPE, MS;FLEMING, TR
通讯作者：
FLEMING, TR
影响因子：
2.1
作者：
Tian, Lu;Zhao, Lihui;Wei, L. J.
通讯作者：
Wei, L. J.
影响因子：
45.3
作者：
Uno, Hajime;Claggett, Brian;Wei, Lee-Jen
通讯作者：
Wei, Lee-Jen