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Principles of Good Practice for Decision Analytic Modeling in Health-Care Evaluation: Report of the ISPOR Task Force on
Good Research Practices—Modeling Studies
The citation for this report is:
Weinstein MC, O'Brien B, Hornberger
J, et al. Principles of good practice of decision analytic modeling in health
care evaluation: Report of the ISPOR Task Force on Good Research
Practices-Modeling Studies. Value Health 2003; 6:9-17
Task Force Chair
Milton C. Weinstein PhD, Center for Risk Analysis, Harvard School of Public
Health, Boston, Massachusetts, and Innovus Research, Inc., Medford,
Massachusetts, USA.
Leadership Group
Chris McCabe MSc, Senior Lecturer in Health Economics, Trent Institute for
Health Services Research, University of Sheffield, Sheffield, UK.
John Hornberger MD, MS, Acumen, LLC, and Stanford University School of
Medicine, Stanford, California, USA.
Joseph Jackson PhD, Group Director, Pharmaceutical Research Institute,
Bristol-Myers Squibb, Princeton, New Jersey, USA.
Magnus Johannesson PhD, Associate Professor, Centre for Health Economics,
Stockholm School of Economics, Stockholm, Sweden.
Bryan R. Luce PhD, Senior Research Leader and CEO, MEDTAP International,
Bethesda, Maryland
Bernie O’Brien PhD, Professor, Department of Clinical Epidemiology and
Biostatistics, McMaster University, Hamilton, Ontario, Canada
Reviewer Group
Andrea K. Biddle MPH, PhD, Associate Professor, Department of Health Policy
and Administration, School of Public Health, University of North Carolina at
Chapel Hill, Chapel Hill, NC, USA.
Donald Chafin MD, MS, Director, SICU/Associate Professor of Medicine &
Epidemiology, Beth Israel Medical Center/Albert Einstein College of Medicine,
New York, NY, USA.
Daniel Halberg PhD, Assistant Professor, University of Arkansas for Medical
Sciences, Little Rock, Matthew Rousculp MPH, The University of Alabama at
Birmingham, Birmingham, AL, USA.
Phantipa Sathkong MS, Faculty of Pharmaceutical Sciences, Chulalongkorn
University, Pathumwan, Bangkok, Thailand.
Daniel Sarpong PhD, Associate Professor of Biostatistics, College of
Pharmacy, Xavier University of Louisiana, New Orleans, LA, USA.
Hemal Shah PharmD, Director, Boehringer Ingelheim Pharmaceuticals, Inc.,
Ridgefield, CN, USA.
Mendel Singer PhD, Assistant Professor, Case Western Reserve University,
Cleveland, OH, USA.
Dong-Churl Suh PhD, Assistant Professor, Rutgers University, College of
Pharmacy, Piscataway, NJ
John Walt MBA, Manager, Global Pharmacoeconomic Strategy & Research,
Allergan, Irvine, CA, USA.
Leslie Wilson PhD, MS Adjunct Assistant Professor, University of California
San Francisco, San Francisco, California, CA, USA.
ABSTRACT
OBJECTIVES: Mathematical modeling is used widely in economic evaluations of
pharmaceuticals and other health care technologies. Users of models in
government and the private sector need to be able to evaluate the quality of
models according to scientific criteria of good practice. This report describes
the consensus of a task force convened to provide modelers with guidelines for
conducting and reporting modeling studies.
METHODS: The task force was appointed with the advice and consent of the
Board of Directors of ISPOR. Members were experienced developers or users of
models, worked in academia and industry, and came from several countries in
North America and Europe. The task force met on three occasions, conducted
frequent correspondence and exchanges of drafts by electronic mail, and
solicited comments on three drafts from a core group of external reviewers and
more broadly from the membership of ISPOR.
RESULTS: Criteria for assessing the quality of models fell into three areas:
model structure, data used as inputs to models, and model validation. Several
major themes cut across these areas. Models and their results should be
represented as aids to decision making, not as statements of scientific fact;
therefore, it is inappropriate to demand that models be validated prospectively
prior to use. However, model assumptions regarding causal structure and
parameter estimates should be continually assessed against data, and models
revised accordingly. Structural assumptions and parameter estimates should be
reported clearly and explicitly, and opportunities for users to appreciate the
conditional relationship between inputs and outputs should be provided through
sensitivity analyses.
CONCLUSIONS: Model-based evaluations are a valuable resource for health-care
decision makers. It is the responsibility of model developers to conduct
modeling studies according to the best practicable standards of quality and to
communicate results with adequate disclosure of assumptions and with the caveat
that conclusions are conditional upon the assumptions and data upon which the
model is built.
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