Hierarchical Semi-Bayes Methods for Misclassification in Perinatal Epidemiology

• Type of Material: Article
• Authors: Richard F MacLehose, University of Minnesota; Lisa M Bodnar, University of Pittsburgh; Craig Meyer, University of Minnesota; Haitao Chu, University of Minnesota; Timothy Lash, Emory University
• Language: English
• Date: 2018-03-01
• Publisher: Lippincott, Williams & Wilkins
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2018 Wolters Kluwer Health, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1097/EDE.0000000000000789
• Title of Journal or Parent Work: Epidemiology
• ISSN: 1044-3983
• Volume: 29
• Issue: 2
• Start Page: 183
• End Page: 190
• Grant/Funding Information: This project was supported by NIH grant R21 HD065807 and the Thrasher Research Fund (#9181).
• Keywords: Public, Environmental & Occupational Health; SENSITIVITY-ANALYSIS; CANCER; Life Sciences & Biomedicine; EXPOSURE; REGRESSION-ANALYSIS; OBSERVATIONAL DATA; PRIORS; VALIDATION; Science & Technology
• Research Categories: Biology, Biostatistics; Health Sciences, Epidemiology

Abstract

Background: Validation data are used to estimate the extent of misclassification in epidemiologic studies. In the Penn MOMS cohort, prepregnancy body mass index is subject to misclassification, and validation data are available to estimate the extent of misclassification. We use these data to estimate the association between maternal prepregnancy body mass index and early preterm (<32 weeks) birth using a semi-Bayes hierarchical model, allowing for more flexible adjustment for misclassification. Methods: We propose a two-stage model that first fits a Bayesian hierarchical model for the bias parameters in the validation study. This model shrinks bias parameters in different groups toward one another in an effort to gain precision and improve mean squared error. In the second stage, we draw random samples from the posterior distribution of the bias parameters to implement a probabilistic bias analysis adjusting for exposure misclassification in a frequentist outcome model. Results: Bias parameters from the hierarchical model were often more substantively reasonable and often had smaller variance. Adjusting results for misclassification generally attenuated the strength of the unadjusted associations. After adjusting for misclassification, underweight mothers were not at increased risk of early preterm birth relative to normal weight mothers. Severely obese mothers had an increased risk of early preterm birth relative to normal weight mothers. Conclusions: The two-stage semi-Bayesian hierarchical model borrowed strength between group-specific bias parameters to adjust for exposure misclassification. Model results support evidence of an increased risk of early preterm birth among severely obese mothers, relative to normal weight mothers.