Regression for skewed biomarker outcomes subject to pooling. Issue 1 (12th February 2014)

• Record Type: Journal Article
• Authors: Mitchell, Emily M.; Lyles, Robert H.; Manatunga, Amita K.; Danaher, Michelle; Perkins, Neil J.; Schisterman, Enrique F.
• Is Part Of: Biometrics. Volume 70:Issue 1(2014)
• Page Start: 202
• Page End: 211
• Publication Date: 2014-02-12
• Subjects: Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12134
• Languages: English
• ISSNs: 0006-341X

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Summary</title> <sec id="biom12134-sec-0001" sec-type="section"> <p>Epidemiological studies involving biomarkers are often hindered by prohibitively expensive laboratory tests. Strategically pooling specimens prior to performing these lab assays has been shown to effectively reduce cost with minimal information loss in a logistic regression setting. When the goal is to perform regression with a continuous biomarker as the outcome, regression analysis of pooled specimens may not be straightforward, particularly if the outcome is right‐skewed. In such cases, we demonstrate that a slight modification of a standard multiple linear regression model for poolwise data can provide valid and precise coefficient estimates when pools are formed by combining biospecimens from subjects with identical covariate values. When these <alternatives><inline-graphic mimetype="image" xlink:href="ark:/27927/pgg4ss8gb93" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" /><mml:math altimg="urn:x-wiley:15410420:media:biom12134:biom12134-math-0001" display="inline" overflow="scroll" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="bold">x</mml:mi></mml:math></alternatives>‐homogeneous pools cannot be formed, we propose a Monte Carlo expectation maximization (MCEM) algorithm to compute maximum likelihood estimates (MLEs). Simulation studies demonstrate that these analytical methods provide essentially unbiased estimates of coefficient parameters as well as their standard errors when appropriate assumptions are met. Furthermore, we show how one can utilize the fully observed covariate data to inform the pooling strategy, yielding a high level of statistical efficiency at a fraction of the total lab cost.</p> </sec> </abstract>