Department of Epidemiology and Biostatistics, University of South Florida, Tampa, FL, USA.
Stat Methods Med Res. 2018 Dec;27(12):3696-3708. doi: 10.1177/0962280217710679. Epub 2017 May 31.
This paper presents a new development of a bent-cable two-part Tobit model to identify both phasic patterns and mixture of advancing (to AIDS) and non-advancing patients of HIV. In identification of such phasic patterns, estimation of a transition period for the development of drug resistance to antiretroviral (ARV) drug or therapy is carried out using longitudinal data that have a gradual change from a declining phase to an increasing phase. In addition to phasic changes, there are also problems of skewness and left-censoring in the response variable because of a lower limit of detection. A relatively large percentage of data below limit of detection are recorded more than expected under an assumed skew-distribution. To properly accommodate these features, we present an extension of the random effects bent-cable Tobit model that incorporates a mixture of true undetectable observations and those values from a skew-normal distribution for a response with left-censoring, skewness and phasic patterns. The proposed methods are illustrated using real data from an AIDS clinical study.
本文提出了一种新的弯曲电缆两部分 Tobit 模型的发展，以确定 HIV 患者的阶段性模式和进展（向艾滋病）和非进展患者的混合。在识别这种阶段性模式时，使用具有从下降阶段到上升阶段逐渐变化的纵向数据，对抗逆转录病毒（ARV）药物或治疗耐药性的发展进行过渡时期的估计。除了阶段性变化外，由于检测下限，响应变量还存在偏态和左截断问题。由于假定的偏态分布，记录的检测下限以下的较大百分比的数据比预期的要多。为了正确适应这些特征，我们提出了一种扩展的随机效应弯曲电缆 Tobit 模型，该模型包含了真实不可检测观察值和来自具有左截断、偏态和阶段性模式的偏态正态分布的那些值的混合。所提出的方法使用艾滋病临床研究中的真实数据进行了说明。
