Key result
A flexible modelling approach for ROC analysis of diagnostic markers with a probability mass at zero can account for explanatory variables, as illustrated by coronary calcium score data.
This methodological paper provides a flexible statistical approach for analyzing ROC curves of diagnostic markers that have a spike at zero, such as the coronary calcium score.
May aid covariate-adjusted ROC analysis of zero-inflated markers like coronary calcium in research; leaves open clinical validation.
The receiver operating characteristic (ROC) curve and in particular the area under the curve (AUC) is commonly used to examine the discriminatory ability of diagnostic markers. Certain markers while basically continuous and non-negative have a positive probability mass (spike) at the value zero. We discuss a flexible modelling approach to such data and contrast it with the standard non-parametric approach. We show how the modelling approach can be extended to take account of the effect of explanatory variables. We motivate this problem and illustrate the modelling approach using data on the coronary calcium score, measured by electron beam tomography, which is a marker for atherosclerosis.
No takes yet. Share an insight, caveat, or question.
Schisterman et al. (2005) studied Atherosclerosis. Flexible modelling approach for ROC analysis vs. Standard non-parametric approach was evaluated on Discriminatory ability of diagnostic markers (AUC). A flexible modelling approach for ROC analysis of diagnostic markers with a probability mass at zero can account for explanatory variables, as illustrated by coronary calcium score data.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: