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      Let a Thousand Models Bloom: ICER Analytics Opens the Floodgates to Cloud Pseudoscience

      research-article
      , PhD Adjunct Professor
      Innovations in Pharmacy
      University of Minnesota Libraries Publishing
      ICER Analytics, imaginary worlds, pseudoscience, multiple models

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          Abstract

          It has been noted on numerous occasions that modeled claims for cost-effectiveness, if driven by assumption for the lifetime of a hypothetical patient population, can be easily ‘gamed’ to create a required claim. These marketing exercises to support product entry are all too common in the literature. The institute for Clinical and Economic Review (ICER) in its launch of the ICER Analytics platform has provided a framework to support precisely these activities. Following the mainstream methodology in health technology assessment, the ICER Analytics platform facilitates the creation of approximate information to support formulary decisions. This is an odd development because it undercuts ICERs belief that it is the key arbiter in health technology assessment in the US, setting the stage for pricing and access recommendations. With the release of the ICER Analytics platform, others can now customize the ‘backbone’ ICER model in a disease area (i.e., change assumptions) to develop alternative and competing value assessments and ‘fair’ price claims. The problem is, of course, that without a reference point, there is no basis for comparing modeled claims other than through challenging assumptions. Indeed, ICER has made this easy by reducing barriers to lifetime model building so that manufacturers and others can create competing (and confusing) claims within, literally, a few minutes. ICER will then become one of a multitude of competing voices for the attention of formulary committees and other health decision makers; letting a thousand imaginary models bloom where no model can be judged on the basis of credible, empirically evaluable and replicable product claims.

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          Most cited references21

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          The Use of Knowledge

          (1936)
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            • Record: found
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            • Article: not found

            Composite outcome measurement in clinical research: the triumph of illusion over reality?

            Composite measures that combine different types of indicators are widely used in medical research; to evaluate health systems, as outcomes in clinical trials and patient-reported outcome measurement. The potential advantages of such indices are clear. They are used to summarise complex data and to overcome the problem of evaluating new interventions when the most important outcome is rare or likely to occur far in the future. However, many scientists question the value of composite measures, primarily due to inadequate development methodology, lack of transparency or the likelihood of producing misleading results. It is argued that the real problems with composite measurement are related to their failure to take account of measurement theory and the absence of coherent theoretical models that justify the addition of the individual indicators that are combined into the composite index. All outcome measures must be unidimensional if they are to provide meaningful data. They should also have dimensional homogeneity. Ideally, a specification equation should be developed that can predict accurately how organisations or individuals will score on an index, based on their scores on the individual indicators that make up the measure. The article concludes that composite measures should not be used as they fail to apply measurement theory and, consequently, produce invalid and misleading scores.
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              Applying the Rasch model: Fundamental Measurement for the Human Sciences

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                Author and article information

                Journal
                Innov Pharm
                Innov Pharm
                UMLP
                Innovations in Pharmacy
                University of Minnesota Libraries Publishing
                2155-0417
                20 January 2021
                2021
                : 12
                : 1
                : 10.24926/iip.v12i1.3606
                Affiliations
                College of Pharmacy, University of Minnesota
                Author notes
                Corresponding author: Paul C Langley, PhD, Adjunct Professor, College of Pharmacy, University of Minnesota, Minneapolis, MN, Director, Maimon Research LLC; Tucson, AZ. Email: langley@ 123456maimonresearch.com , Website: www.maimonresearch.net
                Article
                jUMLP.v12.i1.pg5
                10.24926/iip.v12i1.3606
                8102958
                096389aa-c2de-4c25-b94b-cfcacafc5a23
                © Individual authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Categories
                Commentary
                Formulary Evaluations

                icer analytics,imaginary worlds,pseudoscience,multiple models

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