A Joint Model for Nonlinear Mixed-Effects Models With Censoring and Covariates Measured With Error, With Application to AIDS Studies
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 ↓
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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
- 研究问题
- HIV病毒载量的纵向分析中，如何同时处理检测限导致的左删失、时变协变量的测量误差和缺失，并刻画患者内外差异？
- 方法与思路
- 作者联合建模协变量与响应过程，在非线性混合效应框架中同时处理三类问题，结合Monte Carlo EM算法和线性化程序估计参数。
- 材料陈述的贡献
- 摘要称相较朴素方法和两步法，该方法产生偏差更小的估计与更可靠的标准误；真实艾滋病数据分析显示可能较好预测未观测载量。
- 阅读关注点
- 核对联合模型的分布、测量误差和缺失机制，删失如何进入估计，以及EM与线性化的近似误差、比较设置和预测验证方式。
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原文摘要
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 measurements may be subject to left censoring due to a detection limit, (b) covariates are often measured with substantial errors, and (c) covariates frequently contain missing data. In this article we address these three problems simultaneously by jointly modeling the covariate and the response processes. We adapt a Monte Carlo EM algorithm and a linearization procedure to estimate the model parameters. Our approach is preferable to naive methods and the two-step method in the sense that it produces less-biased estimates with more-reliable standard errors. We analyze a real AIDS dataset and show that the fitted model may provide good prediction for unobserved viral loads.