Regression for skewed biomarker outcomes subject to pooling

• Published in: Biometrics Vol. 70; no. 1; pp. 202 - 211
• Main Authors: Mitchell, Emily M, Lyles, Robert H, Manatunga, Amita K, Danaher, Michelle, Perkins, Neil J, Schisterman, Enrique F
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishers 01.03.2014; Blackwell Publishing Ltd; International Biometric Society
• Subjects: Abortion, Spontaneous - immunology; Algorithms; Analytical estimating; analytical methods; Bioinformatics; Biomarkers; Biomarkers - analysis; BIOMETRIC PRACTICE; biometry; Chemokine CCL2 - blood; Chemokine CCL2 - immunology; Computer Simulation; Datasets; Design; Efficiency; epidemiological studies; Epidemiology; Estimation methods; Female; Humans; laboratory techniques; Likelihood Functions; Linear Models; Linear regression; MCEM; Monte Carlo Method; Pooled specimens; Pregnancy; Regression analysis; Regression coefficients; Simulations; Skewness; Specimens; Standard error; Design; Biomarkers; MCEM; Pooled specimens; Efficiency; Skewness
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12134

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 x‐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.