Predicting event times in clinical trials when treatment arm is masked
Predicting event times in clinical trials when treatment arm is masked
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DOI：
10.1080/10543400600609445
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发表时间：
2006-05-01
影响因子：
1.1
通讯作者：
Heitjan, DF
中科院分区：
文献类型：
--
作者：
Donovan, JM;Elliott, MR;Heitjan, DF
由于在基于事件的临床试验中，力量主要由事件的数量决定，因此数据的中期或最终分析的时间通常是基于研究过程中事件的累积量来确定的。因此，及早准确地预测具有里程碑意义的临时或终结性事件的时间是很有意义的。现有的贝叶斯方法可以用来预测具有里程碑意义的事件的日期，基于当前的登记人数、事件和后续行动的损失，如果治疗武器已知的话。这项工作将这些方法扩展到通过使用具有已知混合比例的参数混合模型来掩蔽治疗臂的情况。将使用混合模型的后验模拟与假设单个总体的方法进行了比较。混合模型和单种群方法的比较表明，在事件很少的情况下，这两种方法产生的结果有很大的不同，并且随着预测时间越接近标志性事件，这些结果收敛。仿真结果表明，如果存在处理效应，具有扩散先验的混合模型比非混合模型在预测区间内具有更好的覆盖概率。
Because power is primarily determined by the number of events in event-based clinical trials, the timing for interim or final analysis of data is often determined based on the accrual of events during the course of the study. Thus, it is of interest to predict early and accurately the time of a landmark interim or terminating event. Existing Bayesian methods may be used to predict the date of the landmark event, based on current enrollment, event, and loss to follow-up, if treatment arms are known. This work extends these methods to the case where the treatment arms are masked by using a parametric mixture model with a known mixture proportion. Posterior simulation using the mixture model is compared with methods assuming a single population. Comparison of the mixture model with the single-population approach shows that with few events, these approaches produce substantially different results and that these results converge as the prediction time is closer to the landmark event. Simulations show that the mixture model with diffuse priors can have better coverage probabilities for the prediction interval than the nonmixture models if a treatment effect is present.