Dagne Getachew A
a Department of Epidemiology & Biostatistics, College of Public Health, MDC 56 , University of South Florida , Tampa , FL , USA.
J Biopharm Stat. 2018;28(6):1216-1230. doi: 10.1080/10543406.2018.1489407. Epub 2018 Jun 28.
The major limitations of growth curve mixture models for HIV/AIDS data are the usual assumptions of normality and monophasic curves within latent classes. This article addresses these limitations by using non-normal skewed distributions and multiphasic patterns for outcomes of prospective studies. For such outcomes, new skew-t (ST) distributions are proposed for modeling heterogeneous growth trajectories, which exhibit not abrupt but gradual multiphasic changes from a declining trend to an increasing trend over time. We assess these clinically important features of longitudinal HIV/AIDS data using the bent-cable framework within a context of a joint modeling of time-to-event process and response process. A real dataset from an AIDS clinical study is used to illustrate the proposed methods.
用于艾滋病毒/艾滋病数据的生长曲线混合模型的主要局限性在于潜在类别内通常的正态性假设和单相曲线。本文通过对前瞻性研究的结果使用非正态偏态分布和多相模式来解决这些局限性。对于此类结果，提出了新的偏态-t（ST）分布来对异质生长轨迹进行建模，这些轨迹随着时间推移呈现出从下降趋势到上升趋势的不是突然而是逐渐的多相变化。我们在事件发生时间过程和响应过程的联合建模背景下，使用弯曲线框架评估纵向艾滋病毒/艾滋病数据的这些临床重要特征。使用来自一项艾滋病临床研究的真实数据集来说明所提出的方法。
