Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure.

IF 4.7 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Epidemiology Pub Date : 2024-03-07 DOI:10.1097/ede.0000000000001706
Wenze Tang, Donna Spiegelman, Xiaomei Liao, Molin Wang
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Abstract

Regression calibration as developed by Rosner, Spiegelman, and Willett is used to adjust the bias in effect estimates due to measurement error in continuous exposures. The method involves two models: a measurement error model relating the mismeasured exposure to the true (or gold-standard) exposure and an outcome model relating the mismeasured exposure to the outcome. However, no comprehensive guidance exists for determining which covariates should be included in each model. In this article, we investigate the selection of the minimal and most efficient covariate adjustment sets under a causal inference framework. We show that to address the measurement error, researchers must adjust for, in both measurement error and outcome models, any common causes (1) of true exposure and the outcome and (2) of measurement error and the outcome. We also show that adjusting for so-called prognostic variables that are independent of true exposure and measurement error in the 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 nonparametric setting where effect modification by covariates is allowed.
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误测连续暴露回归校准中协变因素的因果选择。
Rosner、Spiegelman 和 Willett 开发的回归校准法用于调整连续暴露测量误差造成的效应估计偏差。该方法涉及两个模型:一个是将误测暴露与真实(或黄金标准)暴露相关联的测量误差模型,另一个是将误测暴露与结果相关联的结果模型。然而,在确定每个模型中应包含哪些协变量方面,目前还没有全面的指导。在本文中,我们研究了在因果推理框架下如何选择最小和最有效的协变量调整集。我们表明,为了解决测量误差问题,研究人员必须在测量误差模型和结果模型中调整(1)真实暴露和结果的共同原因以及(2)测量误差和结果的共同原因。我们还表明,在结果模型中调整与真实暴露和测量误差无关的所谓预后变量可能会提高效率,而调整仅与真实暴露相关的任何协变量通常会导致现实环境中的效率损失。我们将提出的协变量选择方法应用于健康专业人员随访研究数据集,研究纤维摄入量对心血管疾病的影响。最后,我们将最初提出的估计方法扩展到允许协变量影响修正的非参数环境中。
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来源期刊
Epidemiology
Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.70
自引率
3.70%
发文量
177
审稿时长
6-12 weeks
期刊介绍: Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.
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