Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure
连续暴露测量误差下回归校准的因果协变量选择AI 解读 ↓
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连续暴露测量误差下回归校准的因果协变量选择
- 研究问题
- 使用回归校准纠正连续暴露的测量误差时，测量误差模型与结果模型分别应纳入哪些协变量，才能兼顾偏差控制与效率？
- 方法与思路
- 作者在因果推断框架下分析最小及高效调整集，比较不同协变量进入两个模型的作用，并扩展到允许效应修饰的非参数情形。
- 材料陈述的贡献
- 摘要称明确了两类共同原因的调整要求，讨论变量仅在主研究可用时的处理及效率影响，并应用于纤维摄入与心血管疾病研究。
- 阅读关注点
- 应核对因果图、校准数据与主研究的关系、弱测量误差关联的判据，以及协变量选择结论和非参数扩展各自需要的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未完整列出识别条件，也未量化弱关联要求或效率变化。
原文摘要
Regression calibration as developed by Rosner, Spiegelman and Willet is used to correct the bias in effect estimates due to measurement error in continuous exposures. The method involves two models: a measurement error model (MEM) relating the mismeasured exposure to the true exposure and an outcome model relating the mismeasured exposure to outcome. However, no comprehensive guidance exists for determining which covariates should be included in each model. In this paper, we investigate the selection of the minimal and most efficient covariate adjustment sets under a causal inference framework. We show that in order to correct for the measurement error, researchers must adjust for, in both MEM and outcome model, any common causes (1) of true exposure and the outcome and (2) of measurement error and the outcome. When such variable(s) are only available in the main study, researchers should still adjust for them in the outcome model to reduce bias, provided that these covariates are at most weakly associated with measurement error. We also show that adjusting for so called prognostic variables that are independent of true exposure and measurement error in outcome model, may increase efficiency, while adjusting for any covariates that are associated only with true exposure generally results in efficiency loss in realistic settings. We apply the proposed covariate selection approach to the Health Professional Follow-up Study dataset to study the effect of fiber intake on cardiovascular disease. Finally, we extend the originally proposed estimators to a non-parametric setting where effect modification by covariates is allowed.