Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome.
The positive and negative predictive values are standard measures used to quantify the predictive accuracy of binary biomarkers when the outcome being predicted is also binary. When the biomarkers are instead being used to predict a failure time outcome, there is no standard way of quantifying predictive accuracy. We propose a natural extension of the traditional predictive values to accommodate censored survival data. We discuss not only quantifying predictive accuracy using these extended predictive values, but also rigorously comparing the accuracy of two biomarkers in terms of their predictive values. Using a marginal regression framework, we describe how to estimate differences in predictive accuracy and how to test whether the observed difference is statistically significant.
Moskowitz
Chaya S
CS
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 East 63rd Street, 3rd Floor, New York, NY 10021, USA. moskowxc1@mskcc.org
Pepe
Margaret S
MS
D015415:Biomarkers; D001943:Breast Neoplasms / Q000601:surgery; D003198:Computer Simulation; D005260:Female; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology; D008954:Models, Biological; D015233:Models, Statistical; D009154:Mutation; D009364:Neoplasm Recurrence, Local / Q000473:pathology; D011237:Predictive Value of Tests; D016019:Survival Analysis
D015415:Biomarkers