Longitudinal Studies With Outcome-Dependent Follow-up
提出贝叶斯参数和半参数部分线性回归方法，分析随访时间依赖历史结果和测量时间的纵向数据，通过模拟和实例验证了方法的准确性。
AbstractWe propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing subject-specific correlations for the longitudinal response and by introducing a subject-specific latent variable to accommodate the association between the longitudinal measurements and the follow-up times. An extensive simulation study shows that our Bayesian partially linear regression method facilitates accurate estimation of the true regression line and the regression parameters. We illustrate our new methodology using data from a longitudinal observational study.KEY WORDS: Bayesian cubic smoothing splineLatent variablePartially linear model