Analysis of exacerbation rates in asthma and chronic obstructive pulmonary disease: example from the TRISTAN study
Analysis of exacerbation rates in asthma and chronic obstructive pulmonary disease: example from the TRISTAN study
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DOI：
10.1002/pst.250
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发表时间：
2007-04-01
影响因子：
1.5
通讯作者：
Anderson, Julie
中科院分区：
文献类型：
--
作者：
Keene, Oliver N.;Jones, Mark R. K.;Anderson, Julie
临床试验中的复发事件通常使用多个事件间隔时间方法或基于事件数量分布的直接方法进行分析。这些方法的一个应用领域是来自呼吸系统临床试验的恶化数据。对慢性阻塞性肺疾病(COPD)的大型试验(n=1465)，说明了不同的分析方法和涉及的问题。对于恶化率，临床的兴趣集中在每种治疗的直接比较上，这有利于基于分布的分析，而不是事件发生时间的方法。泊松回归经常被使用，最近被推荐为COPD恶化的适当分析方法，但对于这种分析，关键的假设往往显得不合理。相比之下，负二项模型的使用提供了一种更有吸引力的方法，该模型对应于为每个受试者假设一个单独的泊松参数。非参数方法避免了这些模型所要求的一些假设，但由于数据的离散和有界的性质，不能提供对治疗效果的适当估计。版权所有(C)2007 John Wiley&Sons，Ltd.
Recurrent events in clinical trials have typically been analysed using either a multiple time-to-event method or a direct approach based on the distribution of the number of events. An area of application for these methods is exacerbation data from respiratory clinical trials. The different approaches to the analysis and the issues involved are illustrated for a large trial (n = 1465) in chronic obstructive pulmonary disease (COPD). For exacerbation rates, clinical interest centres on a direct comparison of rates for each treatment which favours the distribution-based analysis, rather than a time-to-event approach. Poisson regression has often been employed and has recently been recommended as the appropriate method of analysis for COPD exacerbations but the key assumptions often appear unreasonable for this analysis. By contrast use of a negative binomial model which corresponds to assuming a separate Poisson parameter for each subject offers a more appealing approach. Nonparametric methods avoid some of the assumptions required by these models, but do not provide appropriate estimates of treatment effects because of the discrete and bounded nature of the data. Copyright (c) 2007 John Wiley & Sons, Ltd.