Estimating Outcome-Exposure Associations when Exposure Biomarker Detection Limits vary Across Batches.

ABSTRACT: Limit of detection (LOD) issues are ubiquitous in exposure assessment. Although there is an extensive literature on modeling exposure data under such imperfect measurement processes, including likelihood-based methods and multiple imputation, the standard practice continues to be naïve single imputation by a constant (e.g., (Equation is included in full-text article.)). In this article, 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 LOD and the proportion of censored observations differ across batches. Compounding this problem is the potential for nonrandom assignment of samples to each batch, often driven by enrollment patterns and biosample storage. This issue is

SUBMITTER: Boss J 

PROVIDER: S-EPMC6677587 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature