Combining longitudinal discriminant analysis and partial area under the ROC curve to predict non-response to
Esther Lukasiewicz1, Malka Gorfine, Avidan U Neumann
1Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel and Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel. estherh@gertner.health.gov.il
Abstract:
A longitudinal discriminant analysis is applied to build predictive models based on repeated measurements of serum hepatitis C virus RNA. These models are evaluated through the partial area under the receiver operating curve index (PA index) and, the final selection of the best model is based on cross-validated estimates of the PA index. Models are compared by building 95% bootstrap confidence interval for the difference in PA index between two models. Data from a randomised trial, in which chronic HCV patients were enrolled, are used to illustrate the application of the proposed method to predict treatment outcome.
