Uncertainty Assessment of Input Parameters for Economic Evaluation: Gauss’s Error Propagation, an Alternative to Established Methods

Björn Stollenwerk, Stephanie Stock, Uwe Siebert, Karl W. Lauterbach and Rolf Holle
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Björn Stollenwerk: Institute of Health Economics and Clinical Epidemiology of the University of Cologne, Gleueler Straße Cologne, Germany, bjoern.stollenwerk@helmholtz-muenchen.de, Department of Public Health, Medical Decision Making and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria, Helmholtz Zentrum München (GmbH), Institute of Health Economics and Health Care Management, Neuherberg, Germany
Stephanie Stock: Institute of Health Economics and Clinical Epidemiology of the University of Cologne, Gleueler Straße Cologne, Germany
Uwe Siebert: Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, Department of Public Health, Medical Decision Making and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria
Karl W. Lauterbach: Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, Institute of Health Economics and Clinical Epidemiology of the University of Cologne, Gleueler Straße Cologne, Germany

Medical Decision Making, 2010, vol. 30, issue 3, 304-313

Abstract: In decision modeling for health economic evaluation, bootstrapping and the Cholesky decomposition method are frequently used to assess parameter uncertainty and to support probabilistic sensitivity analysis. An alternative, Gauss’s error propagation law, is rarely known but may be useful in some settings. Bootstrapping, the Cholesky decomposition method, and the error propagation law were compared regarding standard deviation estimates of a hypothetic parameter, which was derived from a regression model fitted to simulated data. Furthermore, to demonstrate its value, the error propagation law was applied to German administrative claims data. All 3 methods yielded almost identical estimates of the standard deviation of the target parameter. The error propagation law was much faster than the other 2 alternatives. Furthermore, it succeeded the claims data example, a case in which the established methods failed. In conclusion, the error propagation law is a useful extension of parameter uncertainty assessment.

Keywords: decision-analytic modeling; health economic evaluation; error propagation law; uncertainty assessment. (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:30:y:2010:i:3:p:304-313

DOI: 10.1177/0272989X09347015

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