Predicting accrual in clinical trials with Bayesian posterior predictive distributions
Predicting accrual in clinical trials with Bayesian posterior predictive distributions
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DOI：
10.1002/sim.3128
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发表时间：
2008-06-15
影响因子：
2
通讯作者：
Carlson, Susan E.
中科院分区：
文献类型：
--
作者：
Gajewski, Byron J.;Simon, Stephen D.;Carlson, Susan E.
研究者需要良好的统计工具来进行临床试验的初始规划和持续监测。特别是，他们需要仔细考虑累积率--患者被招募到临床试验中的速度。缓慢的累积降低了研究在试验结束时提供足够精确（或功效）的结果以做出有意义的科学推断的可能性。在本文中，我们提出了一种预测应计利润的方法。使用贝叶斯框架，我们将联合收割机先验信息与监测点已知的信息相结合，以获得预测。我们提供后验预测分布的应计。该方法是有吸引力的，因为它占参数和抽样分布的不确定性。我们用实际应计数据说明这种方法，并讨论围绕应计问题的实际点。版权所有（C）2007约翰威利父子有限公司
Investigators need good statistical tools for the initial planning and for the ongoing monitoring of clinical trials. In particular, they need to carefully consider the accrual rate-how rapidly patients are being recruited into the clinical trial. A slow accrual decreases the likelihood that the research will provide results at the end of the trial with sufficient precision (or power) to make meaningful scientific inferences. In this paper, we present a method for predicting accrual. Using a Bayesian framework we combine prior information with the information known up to a monitoring point to obtain a prediction. We provide posterior predictive distributions of the accrual. The approach is attractive since it accounts for both parameter and sampling distribution uncertainties. We illustrate the approach using actual accrual data and discuss practical points surrounding the accrual problem. Copyright (C) 2007 John Wiley & Sons, Ltd.