Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky 40292.
Department of Biostatistics, University of Florida, Gainesville, Florida 32611.
Stat Med. 2018 Nov 30;37(27):4071-4082. doi: 10.1002/sim.7899. Epub 2018 Jul 12.
The log rank test is a popular nonparametric test for comparing survival distributions among groups. When data are organized in clusters of potentially correlated observations, adjustments can be made to account for within-cluster dependencies among observations, eg, tests derived from frailty models. Tests for clustered data can be further biased when the number of observations within each cluster and the distribution of groups within cluster are correlated with survival times, phenomena known as informative cluster size and informative within-cluster group size. In this manuscript, we develop a log rank test for clustered data that adjusts for the potentially biasing effect of informative cluster size and within-cluster group size. We provide the results of a simulation study demonstrating that our proposed test remains unbiased under cluster-based informativeness, while other candidate tests not accounting for the clustering structure do not properly maintain size. Furthermore, our test exhibits power advantages under scenarios in which traditional tests are appropriate. We demonstrate an application of our test by comparing time to functional progression between groups defined initial functional status in a spinal cord injury data set.
对数秩检验是一种流行的非参数检验方法，用于比较组间的生存分布。当数据按潜在相关观测值的聚类组织时，可以进行调整以考虑观测值之间的聚类内相关性，例如，源自脆弱性模型的检验。当每个聚类内的观测数量和聚类内的组分布与生存时间相关时，聚类数据的检验可能会进一步存在偏差，这种现象称为信息聚类大小和信息聚类内组大小。在本文中，我们开发了一种用于聚类数据的对数秩检验方法，以调整信息聚类大小和聚类内组大小的潜在偏差效应。我们提供了一项模拟研究的结果，表明我们提出的检验在基于聚类的信息性下保持无偏，而其他不考虑聚类结构的候选检验则不能正确保持大小。此外，我们的检验在传统检验适用的情况下具有优势。我们通过比较脊髓损伤数据集初始功能状态定义的组之间的功能进展时间来展示我们的检验的应用。
