Estimating relative risk of a log‐transformed exposure measured in pools

Abstract

Pooling biospecimens prior to performing laboratory assays is a useful tool to reduce costs, achieve minimum volume requirements, and mitigate assay measurement error. When estimating the risk of a continuous, pooled exposure on a binary outcome, specialized statistical techniques are required. Current methods include a regression calibration approach, where the expectation of the individual-level exposure is calculated by adjusting the observed pooled measurement with additional covariate data. While this method employs a linear regression calibration model, we propose an alternative model that can accommodate log-linear relationships between the exposure and predictive covariates. The proposed model permits direct estimation of the relative risk associated with a log-transformation of an exposure measured in pools.

Logistic regression with a continuous exposure measured in pools and subject to errors

“…The discriminant function approach could still be used, however; fitting it separately for cases and controls would produce two odds ratio estimates, whereas fitting it with case status as a covariate would be akin to adjusting for the original outcome variable. Other methods compatible with heterogeneous pools could also be used, although most cannot correct for errors in the pooled biomarker.…”