ArticleAmerican journal of epidemiology2023
Issues in Implementing Regression Calibration Analyses.
Article in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- How can the use of different modes of survey data collection introduce bias? An introduction to mode effects using directed acyclic graphs (DAGs).American journal of epidemiology · 2026Article
- A Latent Variable Approach for Causal Effect Estimation Under Misclassified Treatment Assignment.Statistics in medicine · 2026Article
- What Are We Estimating? Revisiting Standard Nutritional Models Through the Target Trial Framework.American journal of epidemiology · 2026Article
- Causal Covariate Selection for the Regression Calibration Method for Exposure Measurement Error Bias Correction.Statistics in medicine · 2026Article
- Should regression calibration or multiple imputation be used when calibrating different devices in a longitudinal study?American journal of epidemiology · 2025Article
- Practical Considerations for Sandwich Variance Estimation in 2-Stage Regression Settings.American journal of epidemiology · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.
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Registered trials
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