Weibull prediction of event times in clinical trials
Weibull prediction of event times in clinical trials
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DOI：
10.1002/pst.271
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发表时间：
2008-04-01
影响因子：
1.5
通讯作者：
Heitjan, Daniel F.
中科院分区：
文献类型：
--
作者：
Ying, Gui-shuang;Heitjan, Daniel F.
在计划在预先规定的事件计数下进行中期分析的临床试验中，可能希望预测这些标志性事件的时间，作为后勤计划的工具。目前可用的方法使用基于存活指数模型的参数方法（Bagiella和Heitjan，Statistics in Medicine 2001; 20：2055）或基于Kaplan-Meier估计的非参数方法（Ying等人，临床试验2004; 1：352）。Ying等人（2004）证明了这些模型中偏倚和方差之间的权衡;指数方法在假设成立时非常有效，但在假设不成立时可能存在偏倚，而非参数方法偏倚最小，在一系列生存模型下校准良好，但通常提供更宽的预测区间，可能无法在试验早期产生有用的预测。作为一个潜在的妥协，我们建议在这里进行预测下的威布尔生存模型。计算比简单的指数模型更困难，但蒙特卡洛研究表明，在更广泛的假设下，预测是稳健的。我们使用慢性肉芽肿病免疫治疗试验的数据证明了该方法。版权所有（C）2007约翰威利父子有限公司
In clinical trials with interim analyses planned at pre-specified event counts, one may wish to predict the times of these landmark events as a tool for logistical planning. Currently available methods use either a parametric approach based on an exponential model for survival (Bagiella and Heitjan, Statistics in Medicine 2001; 20:2055) or a non-parametric approach based on the Kaplan-Meier estimate (Ying et al., Clinical Trials 2004; 1:352). Ying et al. (2004) demonstrated the trade-off between bias and variance in these models; the exponential method is highly efficient when its assumptions hold but potentially biased when they do not, whereas the non-parametric method has minimal bias and is well calibrated under a range of survival models but typically gives wider prediction intervals and may fail to produce useful predictions early in the trial. As a potential compromise, we propose here to make predictions under a Weibull survival model. Computations are somewhat more difficult than with the simpler exponential model, but Monte Carlo studies show that predictions are robust under a broader range of assumptions. We demonstrate the method using data from a trial of immunotherapy for chronic granulomatous disease. Copyright (C) 2007 John Wiley & Sons, Ltd.