Combining longitudinal discriminant analysis and partial area under the ROC curve to predict non-response to treatment for hepatitis C virus

Esther Lukasiewicz, Malka Gorfine, Avidan U. Neumann, Laurence S. Freedman

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)275-289
Number of pages15
JournalStatistical Methods in Medical Research
Volume20
Issue number3
DOIs
StatePublished - Jun 2011
Externally publishedYes

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