TY - JOUR
T1 - Combining longitudinal discriminant analysis and partial area under the ROC curve to predict non-response to treatment for hepatitis C virus
AU - Lukasiewicz, Esther
AU - Gorfine, Malka
AU - Neumann, Avidan U.
AU - Freedman, Laurence S.
PY - 2011/6
Y1 - 2011/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80051909302&partnerID=8YFLogxK
U2 - 10.1177/0962280209341624
DO - 10.1177/0962280209341624
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C2 - 20200199
AN - SCOPUS:80051909302
SN - 0962-2802
VL - 20
SP - 275
EP - 289
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 3
ER -