Correcting for Exposure Misclassification Using Survival Analysis With a Time-Varying Exposure
Document Type
Article
Publication Date
11-2012
Publication Title
Annals of Epidemiology
Abstract
Purpose: Survival analysis is increasingly being used in perinatal epidemiology to assess time-varying risk factors for various pregnancy outcomes. Here we show how quantitative correction for exposure misclassification can be applied to a Cox regression model with a time-varying dichotomous exposure. Methods: We evaluated influenza vaccination during pregnancy in relation to preterm birth among 2267 non-malformed infants whose mothers were interviewed as part of the Slone Birth Defects Study during 2006 through 2011. The hazard of preterm birth was modeled using a time-varying exposure Cox regression model with gestational age as the time-scale. The effect of exposure misclassification was then modeled using a probabilistic bias analysis that incorporated vaccination date assignment. The parameters for the bias analysis were derived from both internal and external validation data. Results: Correction for misclassification of prenatal influenza vaccination resulted in an adjusted hazard ratio (AHR) slightly higher and less precise than the conventional analysis: Bias-corrected AHR 1.04 (95% simulation interval, 0.70-1.52); conventional AHR, 1.00 (95% confidence interval, 0.71-1.41). Conclusions: Probabilistic bias analysis allows epidemiologists to assess quantitatively the possible confounder-adjusted effect of misclassification of a time-varying exposure, in contrast with a speculative approach to understanding information bias.
Recommended Citation
Ahrens, K., Lash, T.L., Louik, C., Mitchell, A.A., & Werler, M.M. (2012). Correcting for exposure misclassification using survival analysis with a time-varying exposure. Annals of Epidemiology, 22(11), 799-806.
Comments
Copyright © 2012 Elsevier Inc. All rights reserved. This project has been funded in whole or in part with Federal funds from the Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority, Department of Health and Human Services, under Contract No. HHSO100201000038C. Data collection was also supported by the following grants: AHRQ 1R18HS018463-01, NICHD 1R01 HD059861, and NICHD 2 R01 HD46595. A preliminary version of this analysis was presented at the Advanced Methods Workshop during the 2011 meeting of the Society for Pediatric and Perinatal Epidemiologic Research (SPER) in Montreal, Canada.