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Adjusting confounders in ranking biomarkers: a model-based ROC approach
Tao Yu1, Jialiang Li, Shuangge Ma
1University of Wisconsin, Madison, USA.
Briefings in Bioinformatics
|March 8, 2012
Summary
This study introduces a novel model-based approach to rank biomarker diagnostic accuracy in complex diseases, adjusting for confounding factors. The method effectively identifies biomarkers with significant predictive power beyond traditional risk factors.
Area of Science:
- Biostatistics
- Genomics
- Medical Informatics
Background:
- High-throughput studies, like gene expression profiling, are crucial for understanding complex human diseases.
- Existing methods often focus on statistical significance rather than diagnostic accuracy and neglect confounding factors.
- Receiver operating characteristic (ROC) approaches are useful but typically do not adjust for confounders.
Purpose of the Study:
- To propose a model-based approach for ranking biomarker diagnostic accuracy using ROC measures.
- To adjust for confounding effects from clinical risk factors and environmental exposures.
- To investigate three different methods for constructing the underlying regression models.
Main Methods:
- Development of a model-based approach incorporating ROC measures.
- Adjustment for confounding variables including clinical risk factors and environmental exposures.
- Investigation of three distinct regression model construction techniques.
Main Results:
- Simulation studies confirmed the proposed methods' ability to identify biomarkers with additional diagnostic power.
- Analysis of two cancer gene-expression studies revealed that adjusting for confounders significantly alters gene rankings.
- The proposed approach provides a more accurate assessment of biomarker utility in disease prediction.
Conclusions:
- The proposed model-based ROC approach effectively ranks biomarker diagnostic accuracy while adjusting for confounders.
- Accounting for confounding factors is essential for accurate biomarker evaluation in high-throughput studies.
- This methodology enhances the reliability of biomarker discovery in complex disease research.
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