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Semiparametric And Nonparametric Methods For Evaluating Risk Prediction Markers In Case-Control Studies, Ying Huang, Margaret Pepe
Semiparametric And Nonparametric Methods For Evaluating Risk Prediction Markers In Case-Control Studies, Ying Huang, Margaret Pepe
UW Biostatistics Working Paper Series
The performance of a well calibrated risk model, Risk(Y)=P(D=1|Y), can be characterized by the population distribution of Risk(Y) and displayed with the predictiveness curve. Better performance is characterized by a wider distribution of Risk(Y), since this corresponds to better risk stratification in the sense that more subjects are identified at low and high risk for the outcome D=1. Although methods have been developed to estimate predictiveness curves from cohort studies, most studies to evaluate novel risk prediction markers employ case-control designs. Here we develop semiparametric and nonparametric methods that accommodate case-control data and assume apriori knowledge of P(D=1). Large and …
Semiparametric Methods For Evaluating The Covariate-Specific Predictiveness Of Continuous Markers In Matched Case-Control Studies, Ying Huang, Margaret S. Pepe
Semiparametric Methods For Evaluating The Covariate-Specific Predictiveness Of Continuous Markers In Matched Case-Control Studies, Ying Huang, Margaret S. Pepe
UW Biostatistics Working Paper Series
To assess the value of a continuous marker in predicting the risk of a disease, a graphical tool called the predictiveness curve has been proposed. It characterizes the marker's predictiveness, or capacity to risk stratify the population by displaying the population distribution of risk endowed by the marker. Methods for making inference about the curve and for comparing curves in a general population have been developed. However, knowledge about a marker's performance in the general population only is not enough. Since a marker's effect on the risk model and its distribution can both differ across subpopulations, its predictiveness may vary …