On the Use of Regression Calibration in a Complex Sampling Design With Application to the Hispanic Community Health Study/Study of Latinos.
Am J Epidemiol
; 190(7): 1366-1376, 2021 07 01.
Article
em En
| MEDLINE
| ID: mdl-33506244
ABSTRACT
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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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Hispânico ou Latino
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Projetos de Pesquisa Epidemiológica
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Análise de Regressão
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Estudos de Amostragem
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Saúde da População
Tipo de estudo:
Clinical_trials
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Diagnostic_studies
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Guideline
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Prognostic_studies
Limite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Am J Epidemiol
Ano de publicação:
2021
Tipo de documento:
Article