A regression approach to ROC surface, with applications to Alzheimer's disease.
We consider the estimation of three-dimensional ROC surfaces for continuous tests given covariates. Three way ROC analysis is important in our motivating example where patients with Alzheimer's disease are usually classified into three categories and should receive different category-specific medical treatment. There has been no discussion on how covariates affect the three way ROC analysis. We propose a regression framework induced from the relationship between test results and covariates. We consider several practical cases and the corresponding inference procedures. Simulations are conducted to validate our methodology. The application on the motivating example illustrates clearly the age and sex effects on the accuracy for Mini-Mental State Examination of Alzheimer's disease.
Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore ; Duke-NUS Graduate Medical School, Singapore ; Singapore Eye Research Institute, Singapore.
Jialiang
J
Li
Singapore Eye Research Institute, Singapore.
Andrew Xiaohua
AX
Zhou
Department of Biostatistics, University of Washington, USA.
Jason P
JP
Fine
bootstrap; maximum likelihood estimation; rank regression; receiver operating characteristic surface; transformation model; volume under ROC surface