Continuous versus categorical data for ROC analysis: some quantitative considerations.

Abstract

RATIONALE AND OBJECTIVES: Several authors have encouraged the use of a quasi-continuous rating scale for data collection in receiver operating characteristic (ROC) curve analysis of diagnostic modalities, rather than rating scales based on five to seven ordinal categories or levels of suspicion. Although many investigators have gone over to this method, a discussion of the issues continues. The present work provides a quantitative analysis from the viewpoint of measurement science. MATERIALS AND METHODS: A simple model of the effect of data discretization or quantization on the measurement of the variance of noisy data was developed. Then Monte Carlo simulations of multiple-reader, multiple-case ROC experiments were performed and analyzed in terms of components-of-variance models to investigate the effect of data quantization in that more complex setting. RESULTS: For single-reader studies, discretization into five categories can reduce the precision of ROC measurements by a large amount. The effect may be attenuated in multireader studies. CONCLUSION: More precise measurements of diagnostic detection performance and thus more efficient use of resources are served by good measurement methods. These are promoted by the use of a quasi-continuous rating scale in ROC studies.