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THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
Rivista di Medicina Nucleare e Imaging Molecolare
A Journal on Nuclear Medicine and Molecular Imaging
Affiliated to the and to the International Research Group of Immunoscintigraphy
Indexed/Abstracted in: Current Contents/Clinical Medicine, EMBASE, PubMed/MEDLINE, Science Citation Index (SciSearch), Scopus
Impact Factor 2,413
ECONOMICS OF NUCLEAR MEDICINE
Guest Editor: Gambhir S. S.
The Quarterly Journal of Nuclear Medicine 2000 June;44(2):197-203
Clinical trials of cost effectiveness in technology evaluation
Valk P. E.
From the Northern California PET Imaging Center Sacramento, CA, USA
and Department of Molecular and Medical Pharmacology UCLA School of Medicine, Los Angeles, CA, USA
This article discusses models of efficacy, design of clinical trials and the role of mathematical modeling in diagnostic technology evaluation and determination of cost-effectiveness. A multi-tiered model of efficacy, which views diagnostic imaging as part of a global process of patient management and outcome, has been described. The first tier involves imaging efficacy, which must be determined by clinical trial. Direct comparison of new and established modalities in a single study population has major advantages over randomized controlled trials, which are extremely costly and time-consuming and are not appropriate for most evaluations of diagnostic modalities. Selection of patients for inclusion in the trial, interpretation and verification of results, and determination of a reference standard are all possible sources of bias, which need to be identified and controlled. Decision analysis modeling can be used to assess diagnostic, therapeutic, patient-outcome and cost efficacy, once imaging efficacy has been evaluated by clinical trial. Decision analysis is easier and less expensive to perform than clinical trials, and results are easily generalizable to other settings. Disadvantages arise from the non-descriptive nature of modeling and lack of transparency, which make it difficult to evaluate the appropriateness of decision tree structures and input data. Modeling is an unavoidable fact of life in technology evaluation, since the resources that would be required for full evaluation of imaging modalities by clinical trial are not available.