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Conditional concordance-assisted learning under matched case-control design for combining biomarkers for population
Wen Li1, Ruosha Li2, Qingxiang Yan3
1Division of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas McGovern Medical School at Houston, Houston, Texas, USA.
Statistics in Medicine
|February 3, 2023
Summary
This study introduces a new distribution-free method for selecting optimal biomarker combinations for early cancer detection. The approach enhances specificity, reducing unnecessary procedures for disease-free individuals.
Area of Science:
- Biostatistics
- Cancer Epidemiology
- Biomarker Discovery
Background:
- Current cancer screening methods have limitations in performance.
- Early cancer detection using biomarkers is crucial for improving outcomes.
- Matched case-control studies are common for biomarker evaluation.
Purpose of the Study:
- To develop a novel method for identifying optimal biomarker combinations for cancer screening.
- To improve the specificity of cancer detection to minimize unnecessary procedures for healthy individuals.
- To address limitations of traditional conditional logistic regression in biomarker analysis.
Main Methods:
- Proposed a conditional concordance-assisted learning method, a distribution-free approach.
- Focused on identifying biomarker combinations with high specificity for cancer screening.
- Analyzed prostate cancer data from the CARET study using the new method.
Main Results:
- The proposed method demonstrated favorable finite sample performance in simulations.
- Established asymptotic properties for the derived optimal biomarker combination.
- Successfully applied the method to real-world prostate cancer screening data.
Conclusions:
- The conditional concordance-assisted learning method offers a robust alternative for biomarker combination selection in cancer screening.
- The method prioritizes high specificity, crucial for population-based screening programs.
- This approach can enhance the discriminative power of biomarkers for early cancer detection.
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