Journal
STATISTICS IN MEDICINE
Volume 23, Issue 10, Pages 1555-1570Publisher
WILEY
DOI: 10.1002/sim.1747
Keywords
classification; correlated data; predictive accuracy; prognosis; sensitivity; survival analysis
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Funding
- NIAID NIH HHS [5-T32-AI7450-08] Funding Source: Medline
- NIGMS NIH HHS [R01 GM54438] Funding Source: Medline
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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. Copyright (C) 2004 John Wiley Sons, Ltd.
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