Thall Peter F, Wooten Leiko H, Tannir Nizar M
Department of Biostatistics and Applied Mathematics, The University of Texas, M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
Clin Trials. 2005;2(6):467-78. doi: 10.1191/1740774505cn121oa.
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.
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.
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.
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.
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.
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.
在许多早期临床试验中，将患者结局表征为二元变量在科学上不合适或在后勤上不可行。在这种情况下，基于事件发生时间变量构建早期停止规则通常更为自然。然而，这种类型的设计可能涉及各种复杂情况。
本文旨在通过示例说明在早期临床试验中监测事件发生时间结局时如何处理可能出现的各种复杂情况。
我们提出了一系列用于肾癌II期临床试验的贝叶斯设计。每个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。第一个设计是最简单的，基于失败时间，定义为这三个事件中的任何一个，假设失败时间呈指数分布，均值具有逆伽马先验。通过模拟将此设计与CMAP设计（Cheung和Thall，《生物统计学》，2002年；58：89 - 97）进行比较。然后依次扩展模型和监测程序以适应几种常见的实际复杂情况，并且我们还研究了该方法的稳健性。
我们的模拟表明：1）可以定期而非连续应用监测规则，而不会实质性降低设计的可靠性；2）考虑到由于定期评估疾病状态导致的区间删失非常重要；3）考虑疾病进展对后续死亡率的影响很重要；4）进行随机试验几乎不会带来额外困难并能提供无偏比较；5）指数 - 逆伽马模型在大多数情况下出人意料地稳健。
这里讨论的方法未考虑患者异质性。这是一个重要但复杂的问题，可以通过扩展此处给出的模型和方法以适应患者协变量和治疗 - 协变量相互作用来处理。
用于监测事件发生时间结局的贝叶斯程序为进行各种早期试验提供了一种实用方法。然而，必须非常谨慎地对当前试验的重要方面进行建模，并校准先验和设计参数，以确保设计具有良好的操作特性。
