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Estimating relative risk of a log‐transformed exposure measured in pools
Author(s) -
Mitchell Emily M.,
Plowden Torie C.,
Schisterman Enrique F.
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7075
Subject(s) - covariate , pooling , statistics , calibration , linear regression , log linear model , computer science , regression analysis , linear model , econometrics , mathematics , artificial intelligence
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. Published 2016. This article is a U.S. Government work and is in the public domain in the USA