A flexible parametric survival model for fitting time to event data in clinical trials
A flexible parametric survival model for fitting time to event data in clinical trials
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DOI：
10.1002/pst.1947
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发表时间：
2019-10-01
影响因子：
1.5
通讯作者：
Liu, Guanghan Frank
中科院分区：
文献类型：
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作者：
Liao, Jason J. Z.;Liu, Guanghan Frank
在临床试验中，评价新药、生物制品或器械的生存获益时，至事件发生时间数据很常见。常用的参数模型，包括指数，威布尔，Gompertz，对数逻辑，对数正态，根本不够灵活，以捕捉复杂的生存曲线在临床和医学研究中观察到的。另一方面，非参数Kaplan Meier（KM）方法在捕捉生存曲线中的各种形状方面非常灵活和成功，但在预测未来事件（例如特定数量事件的时间和特定时间的事件数量）以及预测超出临床试验可用数据范围的事件（例如死亡）风险方面缺乏能力。显然，无论是非参数KM方法还是现有的参数分布都不能满足拟合具有预测有用特征的生存曲线的要求。本文探索并推荐了一种由威布尔分布三个分量混合而成的全参数分布来拟合生存数据，该分布对于观察数据来说与KM一样灵活，但在试验时间之外具有良好的功能，例如预测未来事件、生存概率和危险函数。
Time-to-event data are common in clinical trials to evaluate survival benefit of a new drug, biological product, or device. The commonly used parametric models including exponential, Weibull, Gompertz, log-logistic, log-normal, are simply not flexible enough to capture complex survival curves observed in clinical and medical research studies. On the other hand, the nonparametric Kaplan Meier (KM) method is very flexible and successful on catching the various shapes in the survival curves but lacks ability in predicting the future events such as the time for certain number of events and the number of events at certain time and predicting the risk of events (eg, death) over time beyond the span of the available data from clinical trials. It is obvious that neither the nonparametric KM method nor the current parametric distributions can fulfill the needs in fitting survival curves with the useful characteristics for predicting. In this paper, a full parametric distribution constructed as a mixture of three components of Weibull distribution is explored and recommended to fit the survival data, which is as flexible as KM for the observed data but have the nice features beyond the trial time, such as predicting future events, survival probability, and hazard function.