Correlated biomarker measurement error: an important threat to inference in environmental epidemiology.
Am J Epidemiol
; 177(1): 84-92, 2013 Jan 01.
Article
em En
| MEDLINE
| ID: mdl-23221725
ABSTRACT
Utilizing multiple biomarkers is increasingly common in epidemiology. However, the combined impact of correlated exposure measurement error, unmeasured confounding, interaction, and limits of detection (LODs) on inference for multiple biomarkers is unknown. We conducted data-driven simulations evaluating bias from correlated measurement error with varying reliability coefficients (R), odds ratios (ORs), levels of correlation between exposures and error, LODs, and interactions. Blood cadmium and lead levels in relation to anovulation served as the motivating example, based on findings from the BioCycle Study (2005-2007). For most scenarios, main-effect estimates for cadmium and lead with increasing levels of positively correlated measurement error created increasing downward or upward bias for OR > 1.00 and OR < 1.00, respectively, that was also a function of effect size. Some scenarios showed bias for cadmium away from the null. Results subject to LODs were similar. Bias for main and interaction effects ranged from -130% to 36% and from -144% to 84%, respectively. A closed-form continuous outcome case solution provides a useful tool for estimating the bias in logistic regression. Investigators should consider how measurement error and LODs may bias findings when examining biomarkers measured in the same medium, prepared with the same process, or analyzed using the same method.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
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Viés
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Métodos Epidemiológicos
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Exposição Ambiental
Tipo de estudo:
Prognostic_studies
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Screening_studies
Limite:
Female
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Humans
Idioma:
En
Revista:
Am J Epidemiol
Ano de publicação:
2013
Tipo de documento:
Article
País de afiliação:
Estados Unidos