title: Combining longitudinal discriminant analysis and partial area under the ROC curve to predict non-response to treatment for hepatitis C virus.
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.
authors.affiliation: Faculty 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
authors.forename: Esther
authors.initials: E
authors.lastname: Lukasiewicz
authors.forename: Malka
authors.initials: M
authors.lastname: Gorfine
authors.forename: Avidan U
authors.initials: AU
authors.lastname: Neumann
authors.forename: Laurence S
authors.initials: LS
authors.lastname: Freedman
mesh_terms: D000998:Antiviral Agents / Q000627:therapeutic use; D019540:Area Under Curve; D016001:Confidence Intervals; D016002:Discriminant Analysis; D019698:Hepatitis C, Chronic / Q000188:drug therapy* / Q000821:virology; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D012367:RNA, Viral / Q000097:blood; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D017211:Treatment Failure; D016896:Treatment Outcome; D019562:Viral Load
chemical_list: D000998:Antiviral Agents; D012367:RNA, Viral