Causal Covariate Selection for the Imputation-based Regression Calibration Method for Exposure Measurement Error Bias Correction
暴露测量误差插补型回归校准的因果协变量选择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：摘要以因果图和共同原因界定暴露效应估计的调整集，文本依据直接支持因果推断这一细分方向。
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…
暴露测量误差插补型回归校准的因果协变量选择
- 研究问题
- 校正连续暴露的测量误差时，如何选择最小且有效率的协变量调整集，并协调测量误差模型与结果模型的调整？
- 方法与思路
- 作者利用有向无环图分析真实暴露、测量误差与结果的共同原因，提出两类模型的调整规则，并扩展到存在效应修饰的情形。
- 材料陈述的贡献
- 摘要明确区分两类共同原因的调整要求，提出特定相关变量仅进入测量误差模型的效率建议，并以膳食纤维研究展示应用。
- 阅读关注点
- 应核对调整规则依赖的因果图和测量误差条件、主研究与验证研究的数据要求，以及效应修饰扩展和生物标志物警示的理由。
- 阅读边界
- 这是预印本，同行评审及期刊接收均未核实。摘要未完整列出校准有效性的条件，效率主张也不能推广到所有误差机制。
原文摘要
In this paper, we identify the criteria for the selection of the minimal and most efficient covariate adjustment sets for the regression calibration method developed by Carroll, Rupert and Stefanski (CRS, 1992), used to correct bias due to continuous exposure measurement error. We utilize directed acyclic graphs to illustrate how subject matter knowledge can aid in the selection of such adjustment sets. Valid measurement error correction requires the collection of data on any (1) common causes of true exposure and outcome and (2) common causes of measurement error and outcome, in both the main study and validation study. For the CRS regression calibration method to be valid, researchers need to minimally adjust for covariate set (1) in both the measurement error model (MEM) and the outcome model and adjust for covariate set (2) at least in the MEM. In practice, we recommend including the minimal covariate adjustment set in both the MEM and the outcome model. In contrast with the regression calibration method developed by Rosner, Spiegelman and Willet, it is valid and more efficient to adjust for correlates of the true exposure or of measurement error that are not risk factors in the MEM only under CRS method. We applied the proposed covariate selection approach to the Health Professional Follow-up Study, examining the effect of fiber intake on cardiovascular incidence. In this study, we demonstrated potential issues with a data-driven approach to building the MEM that is agnostic to the structural assumptions. We extend the originally proposed estimators to settings where effect modification by a covariate is allowed. Finally, we caution against the use of the regression calibration method to calibrate the true nutrition intake using biomarkers.