Gamma Models for Estimating the Odds Ratio for a Skewed Biomarker Measured in Pools and Subject to Errors

• Activity Number: 27 - Innovative Methods for Missing Data and Measurement Error in Health Research
• Type: Topic Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Health Policy Statistics Section
• Author(s): Dane Van Domelen and Emily Mitchell and Neil Perkins and Enrique Schisterman and Amita Manatunga and Eugene Huang and Robert H. Lyles*
• Companies: Emory University and Agency for Healthcare Research and Quality (AHRQ) and NICHD and NICHD and Emory University and Emory University and Emory University
• Keywords: biospecimen pooling; discriminant function; logistic regression; measurement error

Abstract

Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design.