Lan Yu, Heitjan Daniel F
1 Department of Statistical Science, Southern Methodist University, Dallas, TX, USA.
2 Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Clin Trials. 2018 Apr;15(2):159-168. doi: 10.1177/1740774517750633. Epub 2018 Jan 29.
In event-based clinical trials, it is common to conduct interim analyses at planned landmark event counts. Accurate prediction of the timing of these events can support logistical planning and the efficient allocation of resources. As the trial progresses, one may wish to use the accumulating data to refine predictions.
Available methods to predict event times include parametric cure and non-cure models and a nonparametric approach involving Bayesian bootstrap simulation. The parametric methods work well when their underlying assumptions are met, and the nonparametric method gives calibrated but inefficient predictions across a range of true models. In the early stages of a trial, when predictions have high marginal value, it is difficult to infer the form of the underlying model. We seek to develop a method that will adaptively identify the best-fitting model and use it to create robust predictions.
At each prediction time, we repeat the following steps: (1) resample the data; (2) identify, from among a set of candidate models, the one with the highest posterior probability; and (3) sample from the predictive posterior of the data under the selected model.
A Monte Carlo study demonstrates that the adaptive method produces prediction intervals whose coverage is robust within the family of selected models. The intervals are generally wider than those produced assuming the correct model, but narrower than nonparametric prediction intervals. We demonstrate our method with applications to two completed trials: The International Chronic Granulomatous Disease study and Radiation Therapy Oncology Group trial 0129.
Intervals produced under any method can be badly calibrated when the sample size is small and unhelpfully wide when predicting the remote future. Early predictions can be inaccurate if there are changes in enrollment practices or trends in survival.
An adaptive event-time prediction method that selects the model given the available data can give improved robustness compared to methods based on less flexible parametric models.
在基于事件的临床试验中，常在预定的标志性事件计数时进行期中分析。准确预测这些事件的发生时间有助于后勤规划和资源的有效分配。随着试验的推进，人们可能希望利用累积的数据来完善预测。
预测事件发生时间的现有方法包括参数治愈和非治愈模型以及涉及贝叶斯自助模拟的非参数方法。当参数方法的基本假设得到满足时，其效果良好，而非参数方法在一系列真实模型中给出的校准预测效率较低。在试验的早期阶段，当预测具有较高的边际价值时，很难推断潜在模型的形式。我们试图开发一种方法，该方法将自适应地识别最佳拟合模型并使用它来创建可靠的预测。
在每个预测时间，我们重复以下步骤：（1）对数据进行重采样；（2）从一组候选模型中识别后验概率最高的模型；（3）在所选模型下从数据的预测后验中进行采样。
一项蒙特卡洛研究表明，自适应方法产生的预测区间在所选模型族内具有稳健的覆盖率。这些区间通常比假设正确模型时产生的区间更宽，但比非参数预测区间更窄。我们通过将我们的方法应用于两项已完成的试验来进行展示：国际慢性肉芽肿病研究和放射治疗肿瘤学组试验0129。
当样本量较小时，任何方法产生的区间都可能校准不佳，而在预测遥远的未来时，区间会宽得毫无帮助。如果入组实践发生变化或生存趋势改变，早期预测可能不准确。
与基于灵活性较低的参数模型的方法相比，一种根据可用数据选择模型的自适应事件时间预测方法可以提高稳健性。
