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Model Parameter Estimation and Uncertainty Analysis

Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6

The citation for this report is:
Briggs AH, Weinstein MC, Fenwick E, et al. Model parameter estimation and uncertainty analysis: A report of the ISPOR-SMDM modeling good research practices task force-6. Value Health 2012;15:835-42.

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For Modeling Good Research Practices – Overview: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1, please see: http://www.ispor.org/workpaper/Modeling-Good-Research-Practices-Overview.asp 

For Conceptualizing a Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2, please see: http://www.ispor.org/workpaper/Conceptualizing-A-Model.asp

For State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3, please see: http://www.ispor.org/workpaper/State-Transition-Modeling.asp
Supplementary Material: http://www.valueinhealthjournal.com/cms/attachment/2003877082/2015551317/mmc1.pdf

For Modeling Using Discrete Event Simulation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4, please see: http://www.ispor.org/workpaper/Modeling-Using-Discrete-Event-Simulation.asp 

For Dynamic Transmission Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-5, please see: http://www.ispor.org/workpaper/Dynamic-Transmission-Modeling.asp

For Model Transparency and Validation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7, please see: http://www.ispor.org/workpaper/Model-Transparency-and-Validation.asp 

Working Group Chair (Lead author):
Andrew Briggs, DPhil, William R. Lindsay Chair of Health Economics, Health Economics & Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, Scotland UK

Working Group Members (Co-authors):
Elizabeth Fenwick PhD, MSc, Lecturer in Health Economics, University of Glasgow, Glasgow, Scotland, United Kingdom
Jonathon Karnon PhD, Professor in Health Economics, University of Adelaide, Adelaide, South Australia
A. David Paltiel PhD, Professor and Acting Division Head, Yale University, Yale School of Public Health, New Haven, CT, USA
Mark Sculpher PhD, MSc, Professor, University of York, Heslington, York, England, United Kingdom
Milton Weinstein PhD, Professor Health Policy & Management, Harvard School of Public Health, Boston, MA US

Goal:
To ensure that good research practices on modeling techniques remain useful for all current modeling techniques as well as to foster the use of model-based results to inform health care decisions, a Modeling Good Research Practices Task Force was created to address (1) advances in modeling, (2) approaches to evaluating variability in models, and (3) transparency in reporting of models. The development of these modeling good research practices is in collaboration with The Society for Medical Decision Making (SMDM). The ISPOR Board of Directors approved this initiative March 25, 2010 and the SMDM Board approved May 1, 2010.

Background:
The first International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices Task Force on Modeling (2000-2003), chaired by Milton Weinstein, published its report in 2003. See Principles of Good Practice for Decision Analytic Modeling in Health-Care Evaluation: Report of the ISPOR Task Force on Good Research Practices—Modeling Studies (pdf format) [1].  This ISPOR Task Force Report began by defining a model and its purposes, presented an approach to evaluating a model, and then described the consensus of the Task Force regarding the attributes that define a good model, in terms of structure, data and validation. 

Developments in three aspects of modeling created a need for a new examination of several issues addressed by the previous Task Force. First, the recommendations were focused on the kinds of models that were in most widespread use in our field at the time, namely state-transition (Markov) and decision-analytic models.  Consideration was given to both deterministic and probabilistic (i.e., individual-level Monte Carlo simulation) approaches to such models. In the intervening years, with advances in computing capabilities, experience with other types of models has grown in our research community, among these being models that focus on the timing of events rather than transitions between health states.  The roles of deterministic cohort models, as well as individual micro-simulations and other techniques such as SEIR transmission models need to be re-examined. Second, analysis of uncertainty surrounding model results has also advanced with computing capabilities, with new approaches to probabilistic sensitivity analysis becoming available.  Still, deterministic sensitivity analyses have an important role to play because of their transparency for decision makers. Finally, while the U.S. Panel on Cost-Effectiveness in Health and Medicine [2] recommended that models be transparent, with detailed technical reports publicly available, this practice has been followed inconsistently.

Activities:
Modeling Good Research Practices - Everything You Need To Know
May 2011 – ISPOR 16th Annual International Meeting, Third Plenary Session, Baltimore, MD, USA

Modeling Good Research Practices (The Good, The Bad, and The Ambiguous)
November 2010 - ISPOR 13th Annual European Congress, Forum Presentation Prague, Czech Republic
2010 - 32nd Annual Meeting of the Society for Medical Decision Making, Toronto, Canada

The draft final report for member comment is: DRAFT Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6.The reviewer comments and author responses to this draft report are: COMMENTS- Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6.


Good Research Practices for Outcomes Research Index