标题： 纵向左删失重复测量的混合模型 显示英文标题

标题： Mixed models for longitudinal left-censored repeated measures

摘要： 纵向研究可能会因左删失的重复测量而变得复杂。例如，在人类免疫缺陷病毒（HIV）感染中，用于量化血浆病毒载量的检测方法存在检测限。简单地将检测限或其一半作为填补值会偏差估计结果及其标准误。本文回顾了两种基于似然的方法，这些方法旨在处理线性混合模型中结果变量的左删失问题。我们展示了如何使用SAS Proc NLMIXED拟合这些模型，并将其与其他程序进行比较。讨论了这些程序的适用范围和局限性，并展示了一个HIV感染领域的实例。 显示英文摘要

摘要： Longitudinal studies could be complicated by left-censored repeated measures. For example, in Human Immunodeficiency Virus infection, there is a detection limit of the assay used to quantify the plasma viral load. Simple imputation of the limit of the detection or of half of this limit for left-censored measures biases estimations and their standard errors. In this paper, we review two likelihood-based methods proposed to handle left-censoring of the outcome in linear mixed model. We show how to fit these models using SAS Proc NLMIXED and we compare this tool with other programs. Indications and limitations of the programs are discussed and an example in the field of HIV infection is shown.