Biard L, Porcher R, Resche-Rigon M
Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, F-75010 Paris, France; Université Paris Diderot - Paris 7, Sorbonne Paris Cité, F-75010 Paris, France; INSERM, ECSTRA Team, UMR-S 1153, F-75010 Paris, France.
Stat Med. 2014 Jul 30;33(17):3047-57. doi: 10.1002/sim.6153. Epub 2014 Mar 28.
When analysing multicentre data, it may be of interest to test whether the distribution of the endpoint varies among centres. In a mixed-effect model, testing for such a centre effect consists in testing to zero a random centre effect variance component. It has been shown that the usual asymptotic χ(2) distribution of the likelihood ratio and score statistics under the null does not necessarily hold. In the case of censored data, mixed-effects Cox models have been used to account for random effects, but few works have concentrated on testing to zero the variance component of the random effects. We propose a permutation test, using random permutation of the cluster indices, to test for a centre effect in multilevel censored data. Results from a simulation study indicate that the permutation tests have correct type I error rates, contrary to standard likelihood ratio tests, and are more powerful. The proposed tests are illustrated using data of a multicentre clinical trial of induction therapy in acute myeloid leukaemia patients.
在分析多中心数据时，检验终点分布在各中心之间是否存在差异可能会很有意义。在混合效应模型中，对这种中心效应进行检验在于将一个随机中心效应方差分量检验为零。已经表明，在原假设下似然比和得分统计量通常的渐近χ²分布不一定成立。在删失数据的情况下，混合效应Cox模型已被用于考虑随机效应，但很少有研究专注于将随机效应的方差分量检验为零。我们提出一种置换检验，通过对聚类指标进行随机置换，来检验多级删失数据中的中心效应。一项模拟研究的结果表明，与标准似然比检验相反，置换检验具有正确的I型错误率，并且更具功效。使用急性髓系白血病患者诱导治疗的多中心临床试验数据对所提出的检验进行了说明。
