Dagne Getachew A, Huang Yangxin
a Department of Epidemiology & Biostatistics, College of Public Health , University of South Florida , Tampa , Florida , USA.
J Biopharm Stat. 2015;25(4):714-30. doi: 10.1080/10543406.2014.920860.
In a longitudinal HIV/AIDS study with response data, observations may be missing because of patient dropouts due to drug intolerance or other problems, resulting in nonignorable missing data. In addition to nonignorable missingness, there are also problems of skewness and left-censoring in the response variable because of a lower limit of detection (LOD). There has been relatively little work published simultaneously dealing with these features of longitudinal data. In particular, one of the features may sometimes be the existence of a larger proportion of left-censored data falling below LOD than expected under a usually assumed log-normal distribution. When this happens, an alternative model that can account for a high proportion of censored data should be considered. We present an extension of the random effects Tobit model that incorporates a mixture of true undetectable observations and the values from a skew-normal distribution for an outcome with left-censoring, skewness, and nonignorable missingness. A unifying modeling approach is used to assess the impact of left-censoring, skewness, nonignorable missingness and measurement error in covariates on a Bayesian inference. The proposed methods are illustrated using real data from an AIDS clinical study.
在一项带有响应数据的纵向艾滋病毒/艾滋病研究中，由于患者因药物不耐受或其他问题退出研究，观测值可能会缺失，从而导致不可忽视的缺失数据。除了不可忽视的缺失性外，由于检测下限（LOD），响应变量还存在偏度和左删失问题。同时处理纵向数据这些特征的已发表研究相对较少。特别是，其中一个特征有时可能是左删失数据的比例高于通常假设的对数正态分布下预期的比例，且这些数据低于LOD。当这种情况发生时，应考虑一种能够解释高比例删失数据的替代模型。我们提出了随机效应 Tobit 模型的扩展，该模型纳入了真正不可检测观测值的混合以及来自偏态正态分布的值，用于具有左删失、偏度和不可忽视缺失性的结果。一种统一的建模方法用于评估左删失、偏度、不可忽视的缺失性以及协变量中的测量误差对贝叶斯推断的影响。使用来自艾滋病临床研究的真实数据说明了所提出的方法。
