We introduce a general framework for monitoring, modeling, and predicting the recruitment to multi-center clinical trials. The work is motivated by overly optimistic and narrow prediction intervals produced by existing time-homogeneous recruitment models for multi-center recruitment. We first present two tests for detection of decay in recruitment rates, together with a power study. We then introduce a model based on the inhomogeneous Poisson process with monotonically decaying intensity, motivated by recruitment trends observed in oncology trials. The general form of the model permits adaptation to any parametric curve-shape. A general method for constructing sensible parameter priors is provided and Bayesian model averaging is used for making predictions which account for the uncertainty in both the parameters and the model. The validity of the method and its robustness to misspecification are tested using simulated datasets. The new methodology is then applied to oncology trial data, where we make interim accrual predictions, comparing them to those obtained by existing methods, and indicate where unexpected changes in the accrual pattern occur.

中文翻译：

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多中心临床试验的中期招募预测。

我们介绍了一个通用框架，用于监测、建模和预测多中心临床试验的招募情况。这项工作的动机是由现有的多中心招聘的时间同质招聘模型产生的过于乐观和狭窄的预测间隔。我们首先提出了两项​​检测招聘率衰减的测试，以及一项功效研究。然后，我们引入了一个基于具有单调衰减强度的非均匀泊松过程的模型，其动机是在肿瘤学试验中观察到的招募趋势。模型的一般形式允许适应任何参数曲线形状。提供了一种构建合理参数先验的通用方法，并使用贝叶斯模型平均来进行预测，从而解释参数和模型的不确定性。使用模拟数据集测试了该方法的有效性及其对错误指定的鲁棒性。然后将新方法应用于肿瘤学试验数据，我们在其中进行临时应计预测，将它们与现有方法获得的预测进行比较，并指出应计模式发生意外变化的位置。