Monitoring event times in early phase clinical trials: some practical issues
Monitoring event times in early phase clinical trials: some practical issues
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DOI：
10.1191/1740774505cn121oa
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发表时间：
2005-01-01
期刊：
影响因子：
2.7
通讯作者：
Tannir, NM
中科院分区：
文献类型：
--
作者：
Thall, PF;Wooten, LH;Tannir, NM
背景在许多早期临床试验中，将患者结局描述为二元变量在科学上是不合适的或在逻辑上是不可行的。在这种情况下，通常更自然的做法是根据事件发生时间变量来构建提前停止规则。这种类型的设计可能涉及到各种并发症，however.Purpose-本文的目的是举例说明如何可以处理各种并发症时，可能会出现监测时间到事件的结果在早期阶段clinical trial.Methods我们提出了一系列贝叶斯设计的第二阶段临床试验在肾癌。每种设计都包括一个监测严重不良事件、疾病进展和死亡时间的程序。第一种设计是最简单的，它是基于故障时间，定义为三个事件中的任何一个，假设故障时间呈指数分布，平均值为逆伽马先验。通过模拟将该设计与CMAP设计进行比较（Cheung和Thall，Biometrics，2002; 58：89-97）。结果我们的模拟表明：1）可以周期性地而不是连续地应用监测规则，而不会使设计的可靠性发生实质性的下降; 2）由于疾病状态的周期性评估，考虑区间删失是非常重要的; 3）考虑疾病进展对随后死亡率的影响很重要; 4）进行随机试验几乎没有额外的困难，并提供无偏比较; 5）指数-逆伽马模型在大多数情况下令人惊讶地稳健。这是一个重要的，但复杂的问题，可以通过扩展这里给出的模型和方法来处理，以适应患者的协变量和治疗协变量interaction.Conclusions贝叶斯程序监测时间到事件的结果提供了一个实用的方法来进行各种早期试验。然而，必须非常小心地对试验的重要方面进行建模，并校准先验参数和设计参数，以确保设计具有良好的操作特性。
Background In many early phase clinical trials it is scientifically inappropriate or logistically infeasible to characterize patient outcome as a binary variable. In such settings, it often is more natural to construct early stopping rules based on time-to-event variables. This type of design may involve a variety of complications, however.Purpose The purpose of this paper is to illustrate by example how one may deal with various complications that may arise when monitoring time-to-event outcomes in an early phase clinical trial.Methods We present a series of Bayesian designs for a phase II clinical trial in kidney cancer. Each design includes a procedure for monitoring the times to a severe adverse event, disease progression and death. The first design, which is the simplest, is based on the time to failure, defined as any of the three events, assuming exponentially distributed failure times with an inverse gamma prior on the mean. This design is compared by simulation to the CMAP design (Cheung and Thall, Biometrics, 2002; 58: 89-97). The model and monitoring procedure are then extended successively to accommodate several common practical complications, and we also study the method's robustness.Results Our simulations show that 1) one may apply the monitoring rule periodically, rather than continuously, without a substantive degradation of the design's reliability; 2) it is very important to account for interval censoring due to periodic evaluation of disease status; 3) it is important to account for the effect of disease progression on the subsequent death rate; 4) conducting a randomized trial presents little additional difficulty and provides unbiased comparisons; and 5) the exponential-inverse gamma model is surprisingly robust in most cases.Limitations The methods discussed here do not account for patient heterogeneity. This is an important but complex issue that may be dealt with by extending the models and methods given here to accommodate patient covariates and treatment-covariate interaction.Conclusions Bayesian procedures for monitoring time-to-event outcomes offer a practical way to conduct a variety of early phase trials. Considerable care must be given, however, to modeling the important aspects of the trial at hand, and to calibrating the prior and the design parameters to ensure that the design will have good operating characteristics.