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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.
Insights
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.
Abstract:
Interleukin-6 is a biomarker of inflammation which has been suggested as having potential discriminatory ability for myocardial infarction. Because of its high assaying cost it is very expensive to evaluate this marker. In order to reduce this cost we propose pooling the specimens. In this paper we examine the efficiency of ROC curve analysis, specifically the estimation of the area under the ROC curve, when dealing with pooled data. We study the effect of pooling when there are only a fixed number of individuals available for testing and pooling is carried out to save on the number of assays. Alternatively we examine how many pooled assays of size g are necessary to provide essentially the same information as N individual assays. We measure loss of information by means of the change in root mean square error of the estimate of the area under the ROC curve and study the extent of this loss via a simulation study.
