In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status.
翻译:在分析二进制纵向数据时,有必要将反应和共变之间的动态关系作为一种时间函数来模拟,同时调查类似的基于时间的相互作用模式。我们提出了一个新的、通用的、不同系数的模型,其中考虑到受体内变异性,同时组群具有不同系数的功能,但不限制组群的数量,也不过分匹配数据。模型在分析一系列的二进制数据时,提取了人口层次固定效应、组群不同效应和主题随机效应。各种模拟研究显示,在组群数量未知时,正确指定特定组群不同系数的拟议方法的有效性和效用。拟议方法适用于德国社会经济小组(GSOEP)研究的一组混杂的二进制数据,我们在该研究中确定了三个主要组群群,表明社会经济预测器的不同影响是工作状态的年龄函数。