A neutral comparison of statistical methods for time-to-event analyses under non-proportional hazards
非比例风险下生存分析统计方法的中立比较AI 解读 ↓
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非比例风险下生存分析统计方法的中立比较
- 研究问题
- 临床试验存在延迟效应、风险曲线交叉或亚组异质性时，如何在检验功效、效应可解释性及区间覆盖之间选择分析策略？
- 方法与思路
- 作者在多种非比例风险场景中模拟比较加权 log-rank、MaxCombo、RMST、平均风险比、里程碑生存概率及加速失效时间模型。
- 材料陈述的贡献
- 摘要报告功效与解释性的权衡：加权检验通常功效较高，RMST 差更易解释；部分参数方法功效较高但出现偏差和覆盖不足。
- 阅读关注点
- 应核对各方法的原假设与效应目标、权重和生存分布设定，以及模拟场景是否贴近应用，尤其辨别拒绝原假设能否表示治疗获益。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未经核实。摘要未给出具体模拟参数和数值，所述功效比较不能推广为普遍的方法排序。
原文摘要
While well-established methods for time-to-event data are available when the proportional hazards assumption holds, there is no consensus on the best inferential approach under non-proportional hazards (NPH). However, a wide range of parametric and non-parametric methods for testing and estimation in this scenario have been proposed. To provide recommendations on the statistical analysis of clinical trials where non proportional hazards are expected, we conducted a comprehensive simulation study under different scenarios of non-proportional hazards, including delayed onset of treatment effect, crossing hazard curves, subgroups with different treatment effect and changing hazards after disease progression. We assessed type I error rate control, power and confidence interval coverage, where applicable, for a wide range of methods including weighted log-rank tests, the MaxCombo test, summary measures such as the restricted mean survival time (RMST), average hazard ratios, and milestone survival probabilities as well as accelerated failure time regression models. We found a trade-off between interpretability and power when choosing an analysis strategy under NPH scenarios. While analysis methods based on weighted logrank tests typically were favorable in terms of power, they do not provide an easily interpretable treatment effect estimate. Also, depending on the weight function, they test a narrow null hypothesis of equal hazard functions and rejection of this null hypothesis may not allow for a direct conclusion of treatment benefit in terms of the survival function. In contrast, non-parametric procedures based on well interpretable measures as the RMST difference had lower power in most scenarios. Model based methods based on specific survival distributions had larger power, however often gave biased estimates and lower than nominal confidence interval coverage.