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Activity Number: 358 - Contributed Poster Presentations: Section on Statistics in Epidemiology
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #305014
Title: Estimating Outcome-Exposure Associations When Exposure Biomarker Detection Limits Vary Across Batches
Author(s): Jonathan Boss* and Bhramar Mukherjee and Kelly K. Ferguson and Amira M. Aker and Akram N. Alshawabkeh and Jose F. Cordero and John D. Meeker and Sehee Kim
Companies: University of Michigan and University of Michigan and National Institute of Environmental Health Sciences and University of Michigan and Northeastern University and University of Georgia and University of Michigan and University of Michigan
Keywords: Batch effect; Censored likelihood; Left censoring; Limit of detection; Multiple imputation; Preterm delivery

Although limit of detection (LOD) issues are ubiquitous in exposure assessment, replacing values below the LOD with a constant (e.g. LOD/2) continues to be the standard practice in environmental epidemiology. We consider the situation where, due to the practical logistics of data accrual, sampling, and resource constraints, exposure data are analyzed in multiple batches where the LODs and the proportions of censored observations differ across batches. Compounding this problem is the potential for non-random assignment of samples to each batch, often driven by enrollment patterns and biosample storage. We propose a likelihood-based multiple imputation strategy to impute observations that are below the LOD while simultaneously accounting for differential batch assignment. Our simulation study shows that the proposed method has superior estimation properties (i.e., bias, coverage, statistical efficiency) compared to standard alternatives, provided that underlying distributional assumptions are satisfied. We illustrate our method by analyzing data from a cohort study in Puerto Rico that is investigating the relationship between endocrine disruptor exposures and preterm birth.

Authors who are presenting talks have a * after their name.

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