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ROC curve analysis for biomarkers based on pooled assessments
David Faraggi1, Benjamin Reiser, Enrique F Schisterman
1Department of Statistics, University of Haifa, Haifa, Israel.
Statistics in Medicine
|July 23, 2003
Summary
Pooling specimens for Interleukin-6 testing can reduce costs for diagnosing myocardial infarction. This study evaluates the efficiency of ROC curve analysis with pooled data, finding minimal information loss for cost savings.
Area of Science:
- Biomarkers
- Inflammation
- Cardiovascular disease diagnostics
Background:
- Interleukin-6 (IL-6) is a key inflammation biomarker with potential for myocardial infarction (MI) diagnosis.
- High assay costs for IL-6 limit its widespread clinical use for MI.
- Specimen pooling offers a strategy to reduce IL-6 assay expenses.
Purpose of the Study:
- To assess the efficiency of Receiver Operating Characteristic (ROC) curve analysis using pooled data.
- To quantify the impact of specimen pooling on the estimation of the area under the ROC curve (AUC).
- To determine the trade-off between assay cost reduction and information loss due to pooling.
Main Methods:
- Simulation study to evaluate ROC curve analysis with pooled versus individual specimens.
- Analysis of the effect of pooling on the root mean square error (RMSE) of AUC estimation.
- Investigation of the number of pooled assays required to match the information from individual assays.
Main Results:
- Specimen pooling maintains reasonable accuracy in ROC curve analysis for IL-6.
- Information loss, measured by RMSE change, is quantified for various pooling strategies.
- Guidelines are provided on the number of pooled assays needed for equivalent diagnostic information.
Conclusions:
- ROC curve analysis remains effective with pooled specimens for IL-6.
- Specimen pooling is a viable cost-saving method for MI biomarker evaluation.
- The study provides a framework for optimizing pooling strategies to balance cost and diagnostic accuracy.