Robustness of methods for blinded sample size re‐estimation with overdispersed count data
Robustness of methods for blinded sample size re‐estimation with overdispersed count data
复制标题
过度分散计数数据盲法样本量重估方法的稳健性
DOI：
10.1002/sim.5800
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发表时间：
2013
影响因子：
2
通讯作者：
Friede T
中科院分区：
文献类型：
--
作者：
Schneider S;Schmidli H;Friede T
事件计数作为随机临床试验的主要终点越来越常见。由于患者之间的异质性导致方差超过平均值（称为过度离散），因此经常采用通过混合泊松分布来反映这种异质性的统计模型。此类试验规划中的样本量计算需要了解干扰参数，即控制（或总体）事件发生率和过度离散参数。通常，在设计阶段对这些参数的先验知识很少，导致样本大小存在相当大的不确定性。在这种情况下，内部试点研究非常有用，最近针对过度分散的计数数据提出了几种样本量重新估计的盲法，其中之一是基于 EM 算法。在本文中，我们通过研究算法对收敛标准选择的依赖性，研究了基于 EM 算法的程序的实现方面，并发现该程序对与临床实践相关的场景中停止标准的选择敏感。我们还将基于 EM 的程序与其他竞争程序的操作特性（例如样本量分布和功效）进行比较。此外，还探讨了这些程序对模型假设偏差的稳健性。我们发现一些程序至少对于中等程度的偏差是稳健的。结果使用美国国家心、肺和血液研究所资助的无症状心脏缺血试点研究的数据进行说明。版权所有 © 2013 约翰·威利父子有限公司
Counts of events are increasingly common as primary endpoints in randomized clinical trials. With between‐patient heterogeneity leading to variances in excess of the mean (referred to as overdispersion), statistical models reflecting this heterogeneity by mixtures of Poisson distributions are frequently employed. Sample size calculation in the planning of such trials requires knowledge on the nuisance parameters, that is, the control (or overall) event rate and the overdispersion parameter. Usually, there is only little prior knowledge regarding these parameters in the design phase resulting in considerable uncertainty regarding the sample size. In this situation internal pilot studies have been found very useful and very recently several blinded procedures for sample size re‐estimation have been proposed for overdispersed count data, one of which is based on an EM‐algorithm. In this paper we investigate the EM‐algorithm based procedure with respect to aspects of their implementation by studying the algorithm's dependence on the choice of convergence criterion and find that the procedure is sensitive to the choice of the stopping criterion in scenarios relevant to clinical practice. We also compare the EM‐based procedure to other competing procedures regarding their operating characteristics such as sample size distribution and power. Furthermore, the robustness of these procedures to deviations from the model assumptions is explored. We find that some of the procedures are robust to at least moderate deviations. The results are illustrated using data from the US National Heart, Lung and Blood Institute sponsored Asymptomatic Cardiac Ischemia Pilot study. Copyright © 2013 John Wiley & Sons, Ltd.
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影响因子：
4.4
作者：
Sormani, MP;Bruzzi, P;Filippi, L
通讯作者：
Filippi, L
影响因子：
1.5
作者：
Keene, Oliver N.;Jones, Mark R. K.;Anderson, Julie
通讯作者：
Anderson, Julie
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
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影响因子：
1.5
作者：
A. Phillips;O. Keene
通讯作者：
O. Keene
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影响因子：
24.3
作者：
O. Keene;J. Vestbo;Julie A. Anderson;P. Calverley;B. Celli;G. Ferguson;C. Jenkins;P. Jones
通讯作者：
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