TY - JOUR
T1 - Median regression for longitudinal left-censored biomarker data subject to detection limit
AU - Lee, Minjae
AU - Kong, Lan
N1 - Funding Information:
The authors are grateful to the associate editor and the referee for their thoughtful comments and constructive suggestions that have led to considerable improvement of the earlier version. We thank Dr. Derek Angus and the Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center at Department of Critical Care Medicine, University of Pittsburgh for access to the GenIMS data. The GenIMS study was funded by National Institute of General Medical Sciences, National Institutes of Health grant R01 GM61992.
PY - 2011
Y1 - 2011
N2 - Biomarkers are often measured repeatedly in biomedical studies to help understand the development of the disease, identify the patients at high risk, and guide therapeutic strategies for intervention. One common source of measurement error for biomarkers is left-censoring because the assays used may not be sensitive enough to measure concentrations below a detection limit. Likelihood-based approaches that assume multivariate normal distributions have been proposed to account for the left-censoring problem; however, biomarker data are often highly skewed even after transformation. We propose a median regression model that requires minimal assumptions on the distribution and leads to easier interpretation of results in the data's original scale. We develop estimating procedures that incorporate correlations between serial measurements for left-censored longitudinal data. We conduct simulation studies to evaluate the properties of the proposed estimators and to compare median regression models with mixed models under various specifications of distributions and covariance structures. Finally, we demonstrate our method with a dataset from the Genetic and Inflammatory Markers of Sepsis (GenIMS) study.
AB - Biomarkers are often measured repeatedly in biomedical studies to help understand the development of the disease, identify the patients at high risk, and guide therapeutic strategies for intervention. One common source of measurement error for biomarkers is left-censoring because the assays used may not be sensitive enough to measure concentrations below a detection limit. Likelihood-based approaches that assume multivariate normal distributions have been proposed to account for the left-censoring problem; however, biomarker data are often highly skewed even after transformation. We propose a median regression model that requires minimal assumptions on the distribution and leads to easier interpretation of results in the data's original scale. We develop estimating procedures that incorporate correlations between serial measurements for left-censored longitudinal data. We conduct simulation studies to evaluate the properties of the proposed estimators and to compare median regression models with mixed models under various specifications of distributions and covariance structures. Finally, we demonstrate our method with a dataset from the Genetic and Inflammatory Markers of Sepsis (GenIMS) study.
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U2 - 10.1198/sbr.2011.10008
DO - 10.1198/sbr.2011.10008
M3 - Review article
AN - SCOPUS:84867115817
SN - 1946-6315
VL - 3
SP - 363
EP - 371
JO - Statistics in Biopharmaceutical Research
JF - Statistics in Biopharmaceutical Research
IS - 2
ER -