Literature DB >> 16942119

Accuracy versus transparency in pharmacoeconomic modelling: finding the right balance.

David M Eddy1.   

Abstract

As modellers push to make their models more accurate, the ability of others to understand the models can decrease, causing the models to lose transparency. When this type of conflict between accuracy and transparency occurs, the question arises, "Where do we want to operate on that spectrum?" This paper argues that in such cases we should give absolute priority to accuracy: push for whatever degree of accuracy is needed to answer the question being asked, try to maximise transparency within that constraint, and find other ways to replace what we wanted to get from transparency. There are several reasons. The fundamental purpose of a model is to help us get the right answer to a question and, by any measure, the expected value of a model is proportional to its accuracy. Ironically, we use transparency as a way to judge accuracy. But transparency is not a very powerful or useful way to do this. It rarely enables us to actually replicate the model's results and, even if we could, replication would not tell us the model's accuracy. Transparency rarely provides even face validity; from the content expert's perspective, the simplifications that modellers have to make usually raise more questions than they answer. Transparency does enable modellers to alert users to weaknesses in their models, but that can be achieved simply by listing the model's limitations and does not get us any closer to real accuracy. Sensitivity analysis tests the importance of uncertainty about the variables in a model, but does not tell us about the variables that were omitted or the structure of the model. What people really want to know is whether a model actually works. Transparency by itself can't answer this; only demonstrations that the model accurately calculates or predicts real events can. Rigorous simulations of clinical trials are a good place to start. This is the type of empirical validation we need to provide if the potential of mathematical models in pharmacoeconomics is to be fully achieved.

Entities:  

Mesh:

Year:  2006        PMID: 16942119     DOI: 10.2165/00019053-200624090-00002

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  1 in total

1.  Validation of the archimedes diabetes model.

Authors:  David M Eddy; Leonard Schlessinger
Journal:  Diabetes Care       Date:  2003-11       Impact factor: 19.112

  1 in total
  9 in total

Review 1.  Simulation models of obesity: a review of the literature and implications for research and policy.

Authors:  D T Levy; P L Mabry; Y C Wang; S Gortmaker; T T-K Huang; T Marsh; M Moodie; B Swinburn
Journal:  Obes Rev       Date:  2010-10-26       Impact factor: 9.213

2.  Decision-analytic models: current methodological challenges.

Authors:  J Jaime Caro; Jörgen Möller
Journal:  Pharmacoeconomics       Date:  2014-10       Impact factor: 4.981

3.  Continuous time simulation and discretized models for cost-effectiveness analysis.

Authors:  Marta O Soares; Luísa Canto E Castro
Journal:  Pharmacoeconomics       Date:  2012-12-01       Impact factor: 4.981

Review 4.  Dynamic microsimulation models for health outcomes: a review.

Authors:  Carolyn M Rutter; Alan M Zaslavsky; Eric J Feuer
Journal:  Med Decis Making       Date:  2010-05-18       Impact factor: 2.583

Review 5.  Economic evaluation of lifestyle interventions for preventing diabetes and cardiovascular diseases.

Authors:  Sanjib Saha; Ulf-G Gerdtham; Pia Johansson
Journal:  Int J Environ Res Public Health       Date:  2010-08-09       Impact factor: 3.390

6.  Can discrete event simulation be of use in modelling major depression?

Authors:  Agathe Le Lay; Nicolas Despiegel; Clément François; Gérard Duru
Journal:  Cost Eff Resour Alloc       Date:  2006-12-05

Review 7.  Lifestyle Interventions to Prevent Type 2 Diabetes: A Systematic Review of Economic Evaluation Studies.

Authors:  Koffi Alouki; Hélène Delisle; Clara Bermúdez-Tamayo; Mira Johri
Journal:  J Diabetes Res       Date:  2016-01-13       Impact factor: 4.011

8.  Methods to construct a step-by-step beginner's guide to decision analytic cost-effectiveness modeling.

Authors:  Tamlyn Rautenberg; Claire Hulme; Richard Edlin
Journal:  Clinicoecon Outcomes Res       Date:  2016-10-11

9.  The Challenge of Transparency and Validation in Health Economic Decision Modelling: A View from Mount Hood.

Authors:  Seamus Kent; Frauke Becker; Talitha Feenstra; An Tran-Duy; Iryna Schlackow; Michelle Tew; Ping Zhang; Wen Ye; Shi Lizheng; William Herman; Phil McEwan; Wendelin Schramm; Alastair Gray; Jose Leal; Mark Lamotte; Michael Willis; Andrew J Palmer; Philip Clarke
Journal:  Pharmacoeconomics       Date:  2019-11       Impact factor: 4.981

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.