Online Program
Bayesian multivariate hierarchical transformation models for ROC analysis
*James O'Malley, Dartmouth College Keywords: Receiver operating characteristic curve, Box-Cox transformation, Hierarchical model, Optimal diagnostic test A Bayesian multivariate hierarchical transformation model is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. The model incorporates non-linear monotone transformations of the outcomes and allows multiple correlated outcomes may be analyzed. The general framework is illustrated by focusing on the estimation of: (1) the diagnostic accuracy of a covariate-dependent univariate test outcome requiring within-cluster Box-Cox transformations to satisfy the model assumptions; (2) an optimal composite diagnostic test using multivariate clustered outcome data. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-center clinical trial.
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Important Dates & Deadlines
- October 9 - 11, 2013
ICHPS 2013