Dagne Getachew A
College of Public Health, MDC 56, University of South Florida, Tampa, Florida, USA.
J Biopharm Stat. 2022 Mar;32(2):287-297. doi: 10.1080/10543406.2021.2009496. Epub 2022 Feb 15.
This paper presents censored mixture regression models with piecewise growth curves for assessing longitudinal data that exhibit multiphasic features. Such features may include censoring, skewness, measurement errors in covariates, and mixtures of unobserved subpopulations. In the process of describing those features, identification of differential effects of predictors on a response variable for a heterogeneous population (subpopulations) has recently been highly sought. Regression mixture models are key methods for assessing differential effects of predictors. In this article, we extend regression mixture models with normal distribution to incorporate (i) skew-normal distribution, (ii) left-censoring, (iii) measurement errors, and (iv) piecewise growth mixture modeling for describing multiphasic trajectories over time where the observed observations come from a mixture of unobserved subgroups. The proposed methods are illustrated using real data from an AIDS clinical study and a Bayesian approach.
本文提出了带有分段生长曲线的删失混合回归模型，用于评估呈现多阶段特征的纵向数据。这些特征可能包括删失、偏度、协变量中的测量误差以及未观察到的亚群体的混合。在描述这些特征的过程中，最近人们高度寻求识别预测变量对异质群体（亚群体）中响应变量的差异效应。回归混合模型是评估预测变量差异效应的关键方法。在本文中，我们将具有正态分布的回归混合模型进行扩展，以纳入：（i）偏态正态分布，（ii）左删失，（iii）测量误差，以及（iv）用于描述随时间的多阶段轨迹的分段生长混合建模，其中观察到的观测值来自未观察到的亚组的混合。使用来自艾滋病临床研究的真实数据和贝叶斯方法对所提出的方法进行了说明。
