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协变量缺失的非线性混合效应模型：精确与近似推断AI 解读

Nonlinear mixed-effects (NLME) models are popular in many longitudinal studies, including human immunodeficiency virus (HIV) viral dynamics, pharmacokinetic analyses, and studies of growth and decay. In practice, covariates in these studies often contain missing data, and so standard complete-data methods are not directly applicable. In this article we propose Monte Carlo parameter-expanded (PX)-EM algorithms for exact and approximate likelihood inferences for NLME models…

含删失与协变量测量误差的非线性混合效应联合模型：艾滋病研究应用AI 解读

In recent years AIDS researchers have shown great interest in the study of HIV viral dynamics. Nonlinear mixed-effects models (NLMEs) have been proposed for modeling intrapatient and interpatient variations in viral load measurements. The interpatient variation often receives great attention and may be partially explained by time-varying covariates, such as CD4 cell counts. Statistical analyses in these studies are complicated by the following problems: (a) the viral load…