Chebon Sammy, Faes Christel, De Smedt Ann, Geys Helena
Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
Janssen Pharmaceutica NV, Beerse, Belgium.
J Biopharm Stat. 2019;29(6):1043-1067. doi: 10.1080/10543406.2019.1607366. Epub 2019 Apr 27.
Analysis of clustered data is often performed using random effects regression models. In such conditional models, a cluster-specific random effect is often introduced into the linear predictor function. Parameter interpretation of the covariate effects is then conditioned on the random effects, leading to a subject-specific interpretation of the regression parameters. Recently, Marginalized Multilevel Models (MMM) and the Bridge distribution models have been proposed as a unified approach, which allows one to capture the within-cluster correlations by specifying random effects while still allowing for marginal parameter interpretation. In this paper, we investigate these two approaches, and the conditional Generalized Linear Mixed Model (GLMM), in the context of right-truncated, interval-censored time-to-event data, further characterized by clustering and additional overdispersion. While these models have been applied in literature to model the mean, here we extend their application to modeling the hazard function for the survival endpoints. The models are applied to analyze data from the HET-CAM experiment which was designed to assess the potential of a compound to cause injection site reaction. Results show that the MMM and Bridge distribution approaches are useful when interest is in the marginal interpretation of the covariate effects.
聚类数据的分析通常使用随机效应回归模型来进行。在这类条件模型中，通常会将特定于聚类的随机效应引入线性预测函数。协变量效应的参数解释随后以随机效应为条件，从而产生回归参数的特定于个体的解释。最近，边缘化多水平模型（MMM）和桥分布模型已被提出作为一种统一方法，该方法允许通过指定随机效应来捕捉聚类内的相关性，同时仍允许进行边际参数解释。在本文中，我们在右删失、区间删失的事件发生时间数据的背景下研究这两种方法以及条件广义线性混合模型（GLMM），这些数据进一步具有聚类和额外的过度离散特征。虽然这些模型在文献中已被用于对均值进行建模，但在这里我们将它们的应用扩展到对生存终点的危险函数进行建模。这些模型被应用于分析来自HET - CAM实验的数据，该实验旨在评估一种化合物引起注射部位反应的可能性。结果表明，当关注协变量效应的边际解释时，MMM和桥分布方法是有用的。
