A conditional likelihood approach for regression analysis using biomarkers measured with batch-specific error

Authors

  • Ming Wang,

    1. Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, U.S.A.
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  • W. Dana Flanders,

    1. Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, U.S.A.
    2. Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, U.S.A.
    3. Emory Winship Cancer Institute, Atlanta, GA, U.S.A.
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  • Roberd M. Bostick,

    1. Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, U.S.A.
    2. Emory Winship Cancer Institute, Atlanta, GA, U.S.A.
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  • Qi Long

    Corresponding author
    1. Emory Winship Cancer Institute, Atlanta, GA, U.S.A.
    • Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, U.S.A.
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Qi Long, Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, 1518 Clifton Rd NE, Atlanta, GA 30322, U.S.A.

E-mail qlong@emory.edu

Abstract

Measurement error is common in epidemiological and biomedical studies. When biomarkers are measured in batches or groups, measurement error is potentially correlated within each batch or group. In regression analysis, most existing methods are not applicable in the presence of batch-specific measurement error in predictors. We propose a robust conditional likelihood approach to account for batch-specific error in predictors when batch effect is additive and the predominant source of error, which requires no assumptions on the distribution of measurement error. Although a regression model with batch as a categorical covariable yields the same parameter estimates as the proposed conditional likelihood approach for linear regression, this result does not hold in general for all generalized linear models, in particular, logistic regression. Our simulation studies show that the conditional likelihood approach achieves better finite sample performance than the regression calibration approach or a naive approach without adjustment for measurement error. In the case of logistic regression, our proposed approach is shown to also outperform the regression approach with batch as a categorical covariate. In addition, we also examine a ‘hybrid’ approach combining the conditional likelihood method and the regression calibration method, which is shown in simulations to achieve good performance in the presence of both batch-specific and measurement-specific errors. We illustrate our method by using data from a colorectal adenoma study. Copyright © 2012 John Wiley & Sons, Ltd.

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