期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 190, 期 7, 页码 1366-1376出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwab008
关键词
bootstrap; measurement error; multiple imputation; regression calibration
资金
- National Heart, Lung, and Blood Institute [N01-HC65233, N01-HC65234, N01-HC65235, N01-HC65236, N01-HC65237, R01-HL095856]
- National Institute of Allergy and Infectious Diseases [R01-AI131771]
- Patient Centered Outcomes Research Institute [R-1609-36207]
This study investigates variance estimation strategies for regression calibration methods in the context of complex sampling designs, proposing two methods for multistage probability-based sampling designs. The relative performance of these methods is evaluated through simulation studies and analysis of real data from the HCHS/SOL cohort.
Regression calibration is the most widely used method to adjust regression parameter estimates for covariate measurement error. Yet its application in the context of a complex sampling design, for which the common bootstrap variance estimator can be less straightforward, has been less studied. We propose 2 variance estimators for a multistage probability-based sampling design, a parametric and a resampling-based multiple imputation approach, where a latent mean exposure needed for regression calibration is the target of imputation. This work was motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) data from 2008 to 2011, for which relationships between several outcomes and diet, an error-prone self-reported exposure, are of interest. We assessed the relative performance of these variance estimation strategies in an extensive simulation study built on the HCHS/SOL data. We further illustrate the proposed estimators with an analysis of the cross-sectional association of dietary sodium intake with hypertension-related outcomes in a subsample of the HCHS/SOL cohort. We have provided guidelines for the application of regression models with regression-calibrated exposures. Practical considerations for implementation of these 2 variance estimators in the setting of a large multicenter study are also discussed. Code to replicate the presented results is available online.
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