A Bayesian approach for generalized linear models with explanatory biomarker measurement variables subject to detection limit: an application to acute lung injury

• Published in: Journal of applied statistics Vol. 39; no. 8; pp. 1733 - 1747
• Main Authors: Wu, Huiyun, Chen, Qingxia, Ware, Lorraine B., Koyama, Tatsuki
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01.08.2012; Taylor & Francis Ltd
• Subjects: Applied statistics; Bayesian; Bayesian analysis; biomarker; Biomarkers; detection limit; generalized linear model; Generalized linear models; Injuries; Injury prevention; Lung diseases; lung injury; Lungs; Mathematical models; Medical diagnosis; Panels; Studies; Variables
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2012.681362

Biomarkers have the potential to improve our understanding of disease diagnosis and prognosis. Biomarker levels that fall below the assay detection limits (DLs), however, compromise the application of biomarkers in research and practice. Most existing methods to handle non-detects focus on a scenario in which the response variable is subject to the DL; only a few methods consider explanatory variables when dealing with DLs. We propose a Bayesian approach for generalized linear models with explanatory variables subject to lower, upper, or interval DLs. In simulation studies, we compared the proposed Bayesian approach to four commonly used methods in a logistic regression model with explanatory variable measurements subject to the DL. We also applied the Bayesian approach and other four methods in a real study, in which a panel of cytokine biomarkers was studied for their association with acute lung injury (ALI). We found that IL8 was associated with a moderate increase in risk for ALI in the model based on the proposed Bayesian approach.