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The diagnostic likelihood ratio function and modified test for trend: Identifying, evaluating, and validating
Hanna Lindner1, Phyllis A Gimotty1, Warren B Bilker1
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Statistics in Medicine
|September 22, 2023
Summary
This study introduces a new method using the discrete diagnostic likelihood ratio (DLR) function to identify and evaluate both traditional and nontraditional biomarkers, improving biomarker discovery and validation in research.
Area of Science:
- Biostatistics
- Biomarker Discovery
- Medical Informatics
Background:
- Traditional biomarker analysis relies on the assumption of a monotone relationship between biomarker values and disease risk.
- Nontraditional biomarkers, where both low and high values are associated with outcomes, are not well-suited for ROC curve analysis.
- Existing methods for evaluating biomarkers often fail to capture the full spectrum of informative markers.
Purpose of the Study:
- To propose a novel statistical framework for evaluating a wider range of informative biomarkers, including nontraditional ones.
- To enhance the identification, evaluation, and validation of biomarkers in early discovery research.
- To develop methods that can incorporate covariates for improved clinical decision-making.
Main Methods:
- Utilized the discrete diagnostic likelihood ratio (DLR) function, estimated via multinomial logistic regression (MLR).
- Implemented a likelihood ratio test for identifying informative traditional and nontraditional biomarkers.
- Proposed a modified Cochran-Armitage test for trend to categorize informative biomarkers.
Main Results:
- The proposed DLR function effectively evaluates a broader class of biomarkers than traditional ROC-based methods.
- The likelihood ratio test and modified trend test demonstrate statistical properties suitable for biomarker identification and categorization.
- Incorporating covariates into the MLR model yields a covariate-adjusted DLR function for integrated decision-making.
Conclusions:
- The developed statistical methods enable the comprehensive identification, evaluation, and validation of both traditional and nontraditional biomarkers.
- The covariate-adjusted DLR function offers a powerful tool for integrating diverse information in clinical decision-making.
- The approach was successfully applied to gene expression data in high-grade serous ovarian cancer.
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