Nevalainen Jaakko, Oja Hannu, Datta Somnath
School of Health Sciences, University of Tampere, Tampere, Finland.
Department of Mathematics and Statistics, University of Turku, Turku, Finland.
Stat Med. 2017 Jul 20;36(16):2630-2640. doi: 10.1002/sim.7288. Epub 2017 Mar 21.
Clustered data are often encountered in biomedical studies, and to date, a number of approaches have been proposed to analyze such data. However, the phenomenon of informative cluster size (ICS) is a challenging problem, and its presence has an impact on the choice of a correct analysis methodology. For example, Dutta and Datta (2015, Biometrics) presented a number of marginal distributions that could be tested. Depending on the nature and degree of informativeness of the cluster size, these marginal distributions may differ, as do the choices of the appropriate test. In particular, they applied their new test to a periodontal data set where the plausibility of the informativeness was mentioned, but no formal test for the same was conducted. We propose bootstrap tests for testing the presence of ICS. A balanced bootstrap method is developed to successfully estimate the null distribution by merging the re-sampled observations with closely matching counterparts. Relying on the assumption of exchangeability within clusters, the proposed procedure performs well in simulations even with a small number of clusters, at different distributions and against different alternative hypotheses, thus making it an omnibus test. We also explain how to extend the ICS test to a regression setting and thereby enhancing its practical utility. The methodologies are illustrated using the periodontal data set mentioned earlier. Copyright © 2017 John Wiley & Sons, Ltd.
聚类数据在生物医学研究中经常遇到，迄今为止，已经提出了许多方法来分析此类数据。然而，信息性聚类大小（ICS）现象是一个具有挑战性的问题，其存在会影响正确分析方法的选择。例如，Dutta和Datta（2015年，《生物统计学》）提出了一些可以检验的边际分布。根据聚类大小的信息性性质和程度，这些边际分布可能会有所不同，合适检验的选择也是如此。特别是，他们将新检验应用于一个牙周数据集，其中提到了信息性的合理性，但未对其进行正式检验。我们提出了用于检验ICS存在性的自举检验。开发了一种平衡自举方法，通过将重新采样的观测值与紧密匹配的对应值合并来成功估计零分布。依靠聚类内可交换性的假设，即使在聚类数量较少、不同分布以及针对不同备择假设的模拟中，所提出的程序也表现良好，因此使其成为一种综合检验。我们还解释了如何将ICS检验扩展到回归设置，从而提高其实际效用。使用前面提到的牙周数据集对这些方法进行了说明。版权所有© 2017约翰·威利父子有限公司。
