Issues in implementing regression calibration analyses Review


Authors: Boe, L. A.; Shaw, P. A.; Midthune, D.; Gustafson, P.; Kipnis, V.; Park, E.; Sotres-Alvarez, D.; Freedman, L.; on behalf of the Measurement Error and Misclassification Topic Group (TG4) of the STRATOS Initiative
Review Title: Issues in implementing regression calibration analyses
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.
Keywords: protein; risk; measurement; bias (epidemiology); energy; validation; design; measurement error; error; validation studies; confidence-intervals; self-report measures; nutritional epidemiology; berkson error; calibration equation; regression calibration; stratos; initiative; applying recovery biomarkers; explanatory variables
Journal Title: American Journal of Epidemiology
Volume: 192
Issue: 8
ISSN: 0002-9262
Publisher: Oxford University Press  
Date Published: 2023-08-01
Start Page: 1406
End Page: 1414
Language: English
ACCESSION: WOS:001007679300001
DOI: 10.1093/aje/kwad098
PROVIDER: wos
PUBMED: 37092245
PMCID: PMC10666971
Notes: Article -- Source: Wos
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  1. Lillian Augusta Boe
    69 Boe