Title | Issues in Implementing Regression Calibration Analyses. |
Publication Type | Publication |
Year | 2023 |
Authors | Boe LA, Shaw PA, Midthune D, Gustafson P, Kipnis V, Park E, Sotres-Alvarez D, Freedman L, Initiative OBehalf Of |
Journal | Am J Epidemiol |
Volume | 192 |
Issue | 8 |
Pagination | 1406-1414 |
Date Published | 2023 Aug 04 |
ISSN | 1476-6256 |
Keywords | bias, calibration, Humans, Public Health, regression analysis |
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. |
DOI | 10.1093/aje/kwad098 |
Alternate Journal | Am J Epidemiol |
PubMed ID | 37092245 |
PubMed Central ID | PMC10666971 |
Grant List | R01 AI131771 / AI / NIAID NIH HHS / United States 75N92019D00010 / HL / NHLBI NIH HHS / United States N01HC65236 / HL / NHLBI NIH HHS / United States N01HC65235 / HL / NHLBI NIH HHS / United States N01HC65234 / HL / NHLBI NIH HHS / United States P30 CA008748 / CA / NCI NIH HHS / United States N01HC65233 / HL / NHLBI NIH HHS / United States N01HC65237 / HL / NHLBI NIH HHS / United States R37 AI131771 / AI / NIAID NIH HHS / United States |
Issues in Implementing Regression Calibration Analyses.
MS#:
0876
ECI:
Yes
Manuscript Affiliation:
Coordinating Center - Collaborative Studies Coordinating Center - UNC at Chapel Hill
Manuscript Status:
Published