Dynamic RMST curves for survival analysis in clinical trials.
Dynamic RMST curves for survival analysis in clinical trials.
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DOI：
10.1186/s12874-020-01098-5
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发表时间：
2020-08-27
影响因子：
4
通讯作者：
Wu WC
中科院分区：
文献类型：
--
作者：
Liao JJZ;Liu GF;Wu WC
来自免疫肿瘤学（IO）治疗试验的数据通常显示延迟效应、治愈率、交叉危险或这些现象的某种混合。因此，比例风险（PH）假设经常被违反，使得常用的对数秩检验的效力非常不足。在这些试验中，由于缺乏易于理解的解释，用于描述治疗效果的传统风险比可能不是良好的被估量。为了克服这一挑战，在临床文献中强烈建议将限制平均生存时间（RMST）用于生存分析，因为其独立于PH假设以及更具临床意义的解释。RMST还与ICH E-9（R1）中建议的分析相关的被估量以及检测/估计一致性保持良好一致。目前，Kaplan Meier（KM）曲线通常用于RMST相关分析。由于KM方法的一些缺点，例如外推到随访时间以外的时间点的局限性，以及事件数量较少的时间点的较大差异，RMST可能会受到阻碍。本文提出了一种混合模型的动态RMST曲线，以充分增强RMST方法在临床试验中的生存分析。其构造为在限制时间τ的值范围内计算RMST差异或比率，其描绘出随时间推移的演变的治疗效果曲线。这种新的动态RMST曲线克服了KM方法的缺点。通过三个真实的例子说明了该方案的良好性能。RMST为生存期临床试验提供了一种具有临床意义且易于解释的指标。建议的动态RMST方法提供了一个有用的工具，评估治疗效果在不同的时间框架生存的临床试验。该动态RMST曲线还允许检查研究的随访时间是否足够长以证明治疗差异。动态RMST分析的预测功能可用于确定中期分析的适当时间点，数据监查委员会（DMC）可使用该评价工具进行研究建议。
The data from immuno-oncology (IO) therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the proportional hazards (PH) assumption is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the PH assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered. The dynamic RMST curve using a mixture model is proposed in this paper to fully enhance the RMST method for survival analysis in clinical trials. It is constructed that the RMST difference or ratio is computed over a range of values to the restriction time τ which traces out an evolving treatment effect profile over time. This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this proposal is illustrated through three real examples. The RMST provides a clinically meaningful and easily interpretable measure for survival clinical trials. The proposed dynamic RMST approach provides a useful tool for assessing treatment effect over different time frames for survival clinical trials. This dynamic RMST curve also allows ones for checking whether the follow-up time for a study is long enough to demonstrate a treatment difference. The prediction feature of the dynamic RMST analysis may be used for determining an appropriate time point for an interim analysis, and the data monitoring committee (DMC) can use this evaluation tool for study recommendation.
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