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      Unremarked or Unperformed? Systematic Review on Reporting of Validation Efforts of Health Economic Decision Models in Seasonal Influenza and Early Breast Cancer

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          Abstract

          Background

          Transparent reporting of validation efforts of health economic models give stakeholders better insight into the credibility of model outcomes. In this study we reviewed recently published studies on seasonal influenza and early breast cancer in order to gain insight into the reporting of model validation efforts in the overall health economic literature.

          Methods

          A literature search was performed in Pubmed and Embase to retrieve health economic modelling studies published between 2008 and 2014. Reporting on model validation was evaluated by checking for the word validation, and by using AdViSHE (Assessment of the Validation Status of Health Economic decision models), a tool containing a structured list of relevant items for validation. Additionally, we contacted corresponding authors to ask whether more validation efforts were performed other than those reported in the manuscripts.

          Results

          A total of 53 studies on seasonal influenza and 41 studies on early breast cancer were included in our review. The word validation was used in 16 studies (30 %) on seasonal influenza and 23 studies (56 %) on early breast cancer; however, in a minority of studies, this referred to a model validation technique. Fifty-seven percent of seasonal influenza studies and 71 % of early breast cancer studies reported one or more validation techniques. Cross-validation of study outcomes was found most often. A limited number of studies reported on model validation efforts, although good examples were identified. Author comments indicated that more validation techniques were performed than those reported in the manuscripts.

          Conclusions

          Although validation is deemed important by many researchers, this is not reflected in the reporting habits of health economic modelling studies. Systematic reporting of validation efforts would be desirable to further enhance decision makers’ confidence in health economic models and their outcomes.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s40273-016-0410-3) contains supplementary material, which is available to authorized users.

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          Most cited references100

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          Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment.

          The use of decision-analytic modelling for the purpose of health technology assessment (HTA) has increased dramatically in recent years. Several guidelines for best practice have emerged in the literature; however, there is no agreed standard for what constitutes a 'good model' or how models should be formally assessed. The objective of this paper is to identify, review and consolidate existing guidelines on the use of decision-analytic modelling for the purpose of HTA and to develop a consistent framework against which the quality of models may be assessed. The review and resultant framework are summarised under the three key themes of Structure, Data and Consistency. 'Structural' aspects relate to the scope and mathematical structure of the model including the strategies under evaluation. Issues covered under the general heading of 'Data' include data identification methods and how uncertainty should be addressed. 'Consistency' relates to the overall quality of the model. The review of existing guidelines showed that although authors may provide a consistent message regarding some aspects of modelling, such as the need for transparency, they are contradictory in other areas. Particular areas of disagreement are how data should be incorporated into models and how uncertainty should be assessed. For the purpose of evaluation, the resultant framework is applied to a decision-analytic model developed as part of an appraisal for the National Institute for Health and Clinical Excellence (NICE) in the UK. As a further assessment, the review based on the framework is compared with an assessment provided by an independent experienced modeller not using the framework. It is hoped that the framework developed here may form part of the appraisals process for assessment bodies such as NICE and decision models submitted to peer review journals. However, given the speed with which decision-modelling methodology advances, there is a need for its continual update.
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            Dynamic Transmission Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-5

            The transmissible nature of communicable diseases is what sets them apart from other diseases modeled by health economists. The probability of a susceptible individual becoming infected at any one point in time (the force of infection) is related to the number of infectious individuals in the population, will change over time, and will feed back into the future force of infection. These nonlinear interactions produce transmission dynamics that require specific consideration when modeling an intervention that has an impact on the transmission of a pathogen. Best practices for designing and building these models are set out in this article.
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              Cost-effectiveness analyses of vaccination programmes : a focused review of modelling approaches.

              Cost effectiveness is becoming an increasingly important factor for stakeholders faced with decisions about adding a new vaccine into national immunization programmes versus alternative use of resources. Evaluating cost effectiveness, taking into account the relevant biological, clinical, epidemiological and economic factors of a vaccination programme, generally requires use of a model. This review examines the modelling approaches used in cost-effectiveness analyses (CEAs) of vaccination programmes.After overviewing the key attributes of models used in CEAs, a framework for categorising theoretical models is presented. Categories are based on three main attributes: static/dynamic; stochastic/deterministic; and aggregate/individual based. This framework was applied to a systematic review of CEAs of all currently available vaccines for the period of 1976 to May 2007. The systematic review identified 276 CEAs of vaccination programmes. The great majority (83%) of CEAs were conducted in the setting of high-income countries. Only a few vaccines were widely studied, with 57% of available CEAs being focused on the varicella, influenza, hepatitis A, hepatitis B or pneumococcal vaccine. Several time trends were evident, indicating that the number of vaccine CEAs being published is increasing; the main health outcome measures are moving away from the number of cases prevented towards quality-adjusted and unadjusted life-years gained, and more complex models are beginning to be used. The modelling approach was often not adequately described. Of the 208 CEAs that could be categorized according to the framework, around 90% were deterministic, aggregate-level static models. Although a dynamic transmission model is required to account for herd-immunity effects, only 23 of the CEAs were dynamic. None of the CEAs were individual based. To improve communication about the cost effectiveness of vaccination programmes, we believe the first step is for analysts to be more transparent with each other. A clear description of the model type using consistent terminology and justification for the model choice must begin to accompany all CEAs. As a minimum, we urge modellers to provide an explicit statement about the following attributes: static/dynamic; stochastic/deterministic; aggregate/individual based; open/closed. Where relevant, time intervals (discrete/continuous) and (non)linearity should also be described. Enhanced methods of assessing model performance and validity are also required. Our results emphasize the need to improve modelling methods for CEAs of vaccination programmes; specifically, model choice, construction, assessment and validation.
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                Author and article information

                Contributors
                +31 50 363 7576 , p.vemer@rug.nl , healthecon@pepijnvemer.nl
                Journal
                Pharmacoeconomics
                Pharmacoeconomics
                Pharmacoeconomics
                Springer International Publishing (Cham )
                1170-7690
                1179-2027
                29 April 2016
                29 April 2016
                2016
                : 34
                : 833-845
                Affiliations
                [1 ]Department of Pharmacy, PharmacoTherapy, -Epidemiology and -Economics (PTEE), University of Groningen, Groningen, The Netherlands
                [2 ]Pharmacoepidemiology and Clinical Pharmacology, University of Utrecht, Utrecht, The Netherlands
                [3 ]Department of Epidemiology, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB Groningen, The Netherlands
                [4 ]Centre for Nutrition, Prevention and Health Services Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
                Article
                410
                10.1007/s40273-016-0410-3
                4980411
                27129572
                bb0d23f7-62b2-4c88-8cfa-f563581d24eb
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Categories
                Systematic Review
                Custom metadata
                © Springer International Publishing Switzerland 2016

                Economics of health & social care
                Economics of health & social care

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