Biometrical Journal · 2004 · 22 citations · 5 references
Public HealthBiostatisticsRadiologyBiomedical ImagingDiagnostic ImagingDiagnostic RadiologyLongitudinal Data AnalysisLongitudinal Dose-response AnalysisMixed Model AnalysisMixed-effects ModelsMultivariate AnalysisRandom Effects ModelingCovariance StructureCovariance Structure AnalysisCovariance Structure ModelingFixed Effects EstimationLinear Discriminant AnalysisMixed Model MethodologyRepeated MeasurementsSubject-specific Effects
Abstract In medical diagnostics data are often characterized by repeated observations on the same subject (patient), particularly in form of time series, dose‐response curves etc. If the aim is an individual allocation of patients into one of different groups (diagnoses), a discriminant analysis has to be applied. In a first attempt, Tomasko et al., 1999 [5] modified the well‐known linear discriminant analysis using the mixed model MANOVA for the estimation of fixed effects and for a determination of various structures of covariance matrices, including unstructured, compound symmetry, and autoregressive of order 1. We extended that approach by a random subject effect (Azzalani, 1995 [1]), validated error estimations, and cross‐validated feature selection (Wernecke, 2002 [9]). In this paper, we complete the investigations of the new method with a cross‐validated selection of the adequate model (covariance structure) and extensive Monte‐Carlo simulations. An application to contrast‐enhanced breast MRI signals in a multicenter study is also given. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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Estimation of Error Rates in Discriminant Analysis
Peter A. Lachenbruch, M. R. Mickey · Technometrics · 1968 · 1.5K citations
Sylvia H. Heywang‐Köbrunner, Ulrich Bick, William G. Bradley et al. · European Radiology · 2001 · 160 citations