Modeling Repeated Measures With Monotonic Ordinal Responses and Misclassification, With Applications to Studying Maturation
提出一类模型分析重复测量的单调有序响应数据，同时处理诊断误分类问题，并用EM算法估计，应用于研究种族和年龄对青春期成熟的影响。
Abstract Many longitudinal studies of children are concerned with the modeling of monotonic responses such as growth and sexual maturation. The National Heart, Lung, and Blood Institute Growth and Health Study (NGHS) is a longitudinal study designed to examine the effect of growth and maturation on the development of obesity and related cardiovascular risk factors among black and white adolescent girls. Sexual maturation is measured with an ordinal outcome and is known to be measured with sizable diagnostic error. Of interest is examining the effects of race and age on the sexual maturation process. Here we propose a class of models for analyzing repeated monotonic ordinal responses with diagnostic misclassification in which we separately model the underlying monotonic response and misclassification processes. We develop an EM algorithm for maximum likelihood estimation that incorporates covariates and randomly missing data. We use the method to analyze the NGHS sexual maturation data.