Semiparametric regression analysis for composite endpoints subject to componentwise censoring
Semiparametric regression analysis for composite endpoints subject to componentwise censoring
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DOI：
10.1093/biomet/asy013
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发表时间：
2018-06-01
期刊：
影响因子：
2.7
通讯作者：
Ibrahim, Joseph G.
中科院分区：
文献类型：
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作者：
Diao, Guoqing;Zeng, Donglin;Ibrahim, Joseph G.
在临床试验中，带有删失数据的复合终点通常被用作研究结果。例如，无进展生存是一个广泛使用的复合终点，疾病进展和死亡是两个组成部分。无进展生存时间通常被定义为从随机化到疾病进展或因任何原因死亡的较早发生的时间。对于没有经历终点事件的患者，这两个组件的审查时间可能不同。传统的方法，如将两组分的最小审查时间作为无进展生存时间的审查时间，可能会造成效率损失，并可能产生对治疗效果的有偏见的估计。我们提出了一种新的基于可能性的方法，该方法对端点进行分解，并对无进展生存时间和从疾病进展到死亡的时间进行建模。对不同成分的审查时间进行了区分。该方法充分利用了可用的信息，并提供了对治疗效果对无进展生存时间的直接和改进的估计。仿真结果表明，该方法的性能优于其他几种方法，并且对不同的模型误判具有较强的鲁棒性。提供了前列腺癌临床试验的应用程序。
Composite endpoints with censored data are commonly used as study outcomes in clinical trials. For example, progression-free survival is a widely used composite endpoint, with disease progression and death as the two components. Progression-free survival time is often defined as the time from randomization to the earlier occurrence of disease progression or death from any cause. The censoring times of the two components could be different for patients not experiencing the endpoint event. Conventional approaches, such as taking the minimum of the censoring times of the two components as the censoring time for progression-free survival time, may suffer from efficiency loss and could produce biased estimates of the treatment effect. We propose a new likelihood-based approach that decomposes the endpoints and models both the progression-free survival time and the time from disease progression to death. The censoring times for different components are distinguished. The approach makes full use of available information and provides a direct and improved estimate of the treatment effect on progression-free survival time. Simulations demonstrate that the proposed method outperforms several other approaches and is robust against various model misspecifications. An application to a prostate cancer clinical trial is provided.