Correcting for confounding in longitudinal experiments: positioning non-linear mixed effects modeling as implementation of standardization using latent conditional exchangeability
纵向实验的混杂校正：基于潜在条件可交换性的非线性混合效应标准化AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要围绕纵向治疗混杂、标准化与条件可交换性展开因果预测。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
纵向实验的混杂校正：基于潜在条件可交换性的非线性混合效应标准化
- 研究问题
- 在早期结局影响后续治疗安排的纵向研究中，非线性混合效应建模能否作为标准化方法，估计遵循计划治疗的结局？
- 方法与思路
- 作者将混合模型随机效应描述的个体参数用于条件化，在模拟试验的因果图中分别导出潜在与序贯条件可交换性。
- 材料陈述的贡献
- 摘要称非线性混合效应建模可解释为标准化的一种实现，两类条件独立关系均可用于估计目标效应，并在示例中得到无偏估计。
- 阅读关注点
- 核对潜在个体参数如何识别和积分、两类可交换性假设的具体区别，以及治疗与混杂因素反馈下的目标结局定义。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已通过同行评审或被期刊接收；摘要未说明潜在参数识别条件，无偏性不能推广至任意混合模型。
原文摘要
Non-linear mixed effects modeling and simulation (NLME M&S) is evaluated to be used for standardization with longitudinal data in presence of confounders. Standardization is a well-known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that non-linear mixed effects modeling is a particular implementation of standardization that conditions on individual parameters described by the random effects of the mixed effects model. Our motivation is that in pharmacometrics NLME M&S is routinely used to analyze clinical trials and to predict and compare potential outcomes of the same patient population under different treatment regimens. Such a comparison is a causal question sometimes referred to as causal prediction. Nonetheless, NLME M&S is rarely positioned as a method for causal prediction. As an example, a simulated clinical trial is used that assumes treatment confounder feedback in which early outcomes can cause deviations from the planned treatment schedule. Being interested in the outcome for the hypothetical situation that patients adhere to the planned treatment schedule, we put assumptions in a causal diagram. From the causal diagram, conditional independence assumptions are derived either using latent conditional exchangeability, conditioning on the individual parameters, or using sequential conditional exchangeability, conditioning on earlier outcomes. Both conditional independencies can be used to estimate the estimand of interest, e.g., with standardization, and they give unbiased estimates.