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Int. J. Environ. Res. Public Health 2015, 12(11), 14723-14740; doi:10.3390/ijerph121114723

A Discriminant Function Approach to Adjust for Processing and Measurement Error When a Biomarker is Assayed in Pooled Samples

1
Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Mailstop 1518-002-3AA, Atlanta, GA 30322, USA
2
Epidemiology Branch, Division of Intramural Population Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Igor Burstyn and Gheorghe Luta
Received: 3 August 2015 / Revised: 15 October 2015 / Accepted: 6 November 2015 / Published: 18 November 2015
(This article belongs to the Special Issue Methodological Innovations and Reflections-1)
View Full-Text   |   Download PDF [697 KB, uploaded 18 November 2015]

Abstract

Pooling biological specimens prior to performing expensive laboratory assays has been shown to be a cost effective approach for estimating parameters of interest. In addition to requiring specialized statistical techniques, however, the pooling of samples can introduce assay errors due to processing, possibly in addition to measurement error that may be present when the assay is applied to individual samples. Failure to account for these sources of error can result in biased parameter estimates and ultimately faulty inference. Prior research addressing biomarker mean and variance estimation advocates hybrid designs consisting of individual as well as pooled samples to account for measurement and processing (or pooling) error. We consider adapting this approach to the problem of estimating a covariate-adjusted odds ratio (OR) relating a binary outcome to a continuous exposure or biomarker level assessed in pools. In particular, we explore the applicability of a discriminant function-based analysis that assumes normal residual, processing, and measurement errors. A potential advantage of this method is that maximum likelihood estimation of the desired adjusted log OR is straightforward and computationally convenient. Moreover, in the absence of measurement and processing error, the method yields an efficient unbiased estimator for the parameter of interest assuming normal residual errors. We illustrate the approach using real data from an ancillary study of the Collaborative Perinatal Project, and we use simulations to demonstrate the ability of the proposed estimators to alleviate bias due to measurement and processing error. View Full-Text
Keywords: epidemiology; errors-in-variables; odds ratio; pooling epidemiology; errors-in-variables; odds ratio; pooling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lyles, R.H.; Van Domelen, D.; Mitchell, E.M.; Schisterman, E.F. A Discriminant Function Approach to Adjust for Processing and Measurement Error When a Biomarker is Assayed in Pooled Samples. Int. J. Environ. Res. Public Health 2015, 12, 14723-14740.

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