A Two-Step Approach to Measurement Error in Time-Dependent Covariates in Nonlinear Mixed-Effects Models, With Application to IGF-I Pharmacokinetics
非线性混合效应模型中时变协变量测量误差的两步法：IGF-I 药代动力学应用AI 解读 ↓
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非线性混合效应模型中时变协变量测量误差的两步法：IGF-I 药代动力学应用
- 研究问题
- 当时变协变量存在测量误差且其观测时点与药物浓度不同步时，如何分析协变量变化与个体药代动力学行为的关联？
- 方法与思路
- 作者在非线性混合效应模型中加入带测量误差的时变协变量模型，用标准软件实施两步拟合，并应用于 IGF-I 数据及模拟。
- 材料陈述的贡献
- 摘要称，文章提供处理复杂时变协变量与测量误差的两步建模方案，并开展模拟评估，但没有报告具体性能结论。
- 阅读关注点
- 应核对协变量轨迹及误差模型、两步估计的衔接方式和不确定性传递，并查看不同观测时点如何进入拟合过程。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；误差假设、两步算法及模拟结果未展开，不能据此断言优于插值法或解释为因果效应。
原文摘要
The usual approach to the analysis of population pharmacokinetic studies is to represent the concentration-time data by a nonlinear mixed-effects model. Primary objectives are to characterize the pattern of drug disposition in the population and to identify individual-specific covariates associated with pharmacokinetic behavior. We consider data from a study of insulin-like growth factor I (IGF-I) administered by intravenous infusion to patients with severe head trauma. Failure to maintain steady-state levels of IGF-I was thought to be related to the temporal pattern of several covariates measured in the study, and an analysis investigating this issue was of interest. Observations on these potentially relevant covariates for each subject were made at time points different from those at which IGF-I concentrations were determined; moreover, the covariates themselves were likely subject to measurement error. The usual approach to time-dependent covariates in population analysis is to invoke a simple interpolation scheme, such as carrying forward the most recent covariate value, ignoring measurement error; however, for these data, the complicated observed covariate pattern makes this approach suspect. A nonlinear mixed-effects model incorporating a model for time-dependent covariates measured with error is used to describe the IGF-I data, and fitting is accomplished by a two-step strategy implemented using standard software. The performance of the method is evaluated via simulation.