Analysis of Repeated Ordered Categorical Outcomes with Possibly Missing Observations and Time-Dependent Covariates
提出一种分析重复有序分类结果的方法，允许时变协变量和缺失观测，通过两步估计和联合渐近分布来比较两组间的差异及其随时间的变化，适用于纵向研究如空气污染对儿童呼吸健康的影响。
Abstract This article describes a method for comparing responses in two groups of subjects observed repeatedly at a common set of observation times when the response is an ordered categorical outcome. The method, which allows both time-dependent covariates and missing observations, consists of two analytic steps. In the first step, the data are analyzed separately at each occasion using a regression model chosen from the class of models for ordinal data proposed by McCullagh (1980). The joint asymptotic distribution of the estimates of these occasion-specific regression coefficients and a consistent estimator of their asymptotic covariance matrix are obtained without imposing any parametric model of dependence on the repeated observations. In the second step, this asymptotic distribution, together with appropriate simultaneous inference procedures, is used to characterize the overall difference between groups and the variation in group differences over time. The missing-data process may differ between groups to be compared, but it must be independent of response given the covariate values. The new procedures are illustrated by an analysis of annual reports of severity of wheezing in a cohort of preadolescent children participating in a longitudinal study of air pollution and respiratory health.