Suppose that we need to classify a population of subjects into several well-defined ordered risk categories for disease prevention or management with their "baseline" risk factors/markers. In this article, we present a systematic approach to identify subjects using their conventional risk factors/markers who would benefit from a new set of risk markers for more accurate classification. Specifically for each subgroup of individuals with the same conventional risk estimate, we present inference procedures for the reclassification and the corresponding correct re-categorization rates with the new markers. We then apply these new tools to analyze the data from the Cardiovascular Health Study sponsored by the US National Heart, Lung, and Blood Institute. We used Framingham risk factors plus the information of baseline anti-hypertensive drug usage to identify adult American women who may benefit from the measurement of a new blood biomarker, CRP, for better risk classification in order to intensify prevention of coronary heart disease for the subsequent 10 years.
- Coronary heart disease
- Nonparametric functional estimation
- Risk factors/markers and pointwise
- Simultaneous confidence intervals
- Subgroup analysis
ASJC Scopus subject areas
- Applied Mathematics