FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2075388082> ?p ?o ?g. }

• W2075388082 endingPage "793" @default.
• W2075388082 startingPage "792" @default.
• W2075388082 abstract "The ISPOR-SMDM Modeling Good Research Practices Task Force Report in this issue is a welcome update to the previous ISPOR guidance published in 2003 [[1]Weinstein M.C. O'Brien B. Hornberger J. et al.Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR task force on good research practices—modeling studies.Value Health. 2003; 6: 9-17Abstract Full Text PDF PubMed Scopus (914) Google Scholar]. In the past decade, cost-effectiveness analysis has proliferated and its use in coverage and pricing decisions by governmental authorities has spread across much of Europe and elsewhere [[2]Clement F. Harris M.A. Li J.J. et al.Using effectiveness and cost-effectiveness to make drug coverage decisions: a comparison of Britain, Australia, and Canada.JAMA. 2009; 302: 1437-1443Crossref PubMed Scopus (269) Google Scholar]. Modeling methodology has evolved in ways that should improve its usefulness, but despite these enhancements, significant challenges to adoption by US payers remain. The Task Force members have identified several challenges and suggested ways to address barriers to the acceptance of economic models as decision support tools. To do this, it will be necessary to gain a better understanding of the decision support needs of payers. Caro and colleagues [[3]Caro J.J. Briggs A.H. Siebert U. et al.Modeling good research practices—overview: a report of the ISPOR-SMDM modeling good research practices task force-1.Value Health. 2012; 15: 796-803Abstract Full Text Full Text PDF PubMed Scopus (380) Google Scholar] observed that models reduce complex realities to a set of essential elements. Given the complexity of disease, the interpatient variability, and the diversity of health care systems and processes, reducing medical reality and identifying the elements that are essential to a particular health care decision is a daunting task. Creating a useful simulation of a medical decision will always require a great deal of simplification. The Task Force report first addresses identifying the principal determinants of decision outcomes. Decision analysis is most needed when we are least certain how to proceed and probably cannot identify all the relevant elements or understand all the relationships between them. Selecting the best model structure and limiting the number of inputs to reduce complexity to a manageable level may require subjective judgment, upon which stakeholders may disagree. To achieve understanding among stakeholders, a minimum threshold of agreement must be reached. The first step in the logical process outlined by the Task Force is conceptualization [[3]Caro J.J. Briggs A.H. Siebert U. et al.Modeling good research practices—overview: a report of the ISPOR-SMDM modeling good research practices task force-1.Value Health. 2012; 15: 796-803Abstract Full Text Full Text PDF PubMed Scopus (380) Google Scholar]. It is unlikely that a useful model will result without careful thought and planning at its inception. Before drug manufacturers appreciated this, they often left outcomes model planning until after the pivotal trials, only to find that necessary inputs to the model could not be obtained from trial results. Consultation with pharmacoeconomists and decision makers earlier in the product development process can prevent this impasse, but it requires a paradigm shift from trial designs driven by regulatory requirements to trials that support a convincing value proposition. Models must be grounded in clinical reality as experienced by the decision makers who will use them, and model structure must reflect that reality. This involves a trade-off between clarity and complexity. The model should be detailed enough to credibly simulate reality but simple enough to be understood by clinicians with minimal training in economics or decision analysis. This is best achieved by working with end users and incorporating their feedback in successive iterations of the model, a process that requires commitment by both parties. To be helpful, end users must learn enough about models to offer valid criticism, but they need not become pharmacoeconomists. Decision analysts must understand key clinical aspects of the decision problem, but nonclinicians can do this with the help of clinical experts. Decision makers can identify the perspectives, time horizons, comparators, settings, and target populations that will be most useful to them. Traditional audiences for decision models have been clinicians, payers, and policymakers, but the US government's new program of Patient-Centered Outcomes Research [[4]Patient-Centered Outcomes Research InstituteWhat is patient-centered outcomes research?.http://www.pcori.org/patient-centered-outcomes-research/Google Scholar] suggests a future role for patient perspective models as well. Patient-Centered Outcomes Research views the patient as decision maker and principal consumer of decision support information. As more patients accept this role, model builders may be asked to present their evidence in ways that facilitate shared decision making. We should learn to communicate model results in ways that are credible, meaningful, and actionable for nonprofessionals. The last article in this series addresses transparency and validation [[5]Eddy D.M. Hollingworth W. Caro J.J. et al.Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task force-7.Value Health. 2012; 15: 843-850Abstract Full Text Full Text PDF PubMed Scopus (268) Google Scholar]. This is the other point where thoughtful dialogue with decision makers can be useful. Transparency requires both technical and nontechnical model documentation. Most end users will probably want the nontechnical version, but if they have or can consult technical expertise, they will need a detailed methodologic description. The validation process should be transparently described. Clinicians usually focus on reviewing the input data sources and assumptions, clinical credibility of the decision tree, and other aspects about which they are most knowledgeable and may engage a pharmacoeconomist for technical validation. If the methodology includes specific intellectual property, a nondisclosure agreement is appropriate. Many of these concerns about economic models apply to retrospective observational studies as well. Payers expect manufacturers to design studies with a subtle bias in favor of their product, and there is reason for this concern [[6]Neumann P.J. Fang C.H. Cohen J.T. 30 Years of pharmaceutical cost-utility analyses: growth, diversity and methodological improvement.Pharmacoeconomics. 2009; 27: 861-872Crossref PubMed Scopus (67) Google Scholar]. Because ISPOR is also participating in the Comparative Effectiveness Research Collaborative Initiative, the work groups in that project should examine the modeling task force reports for common ground. Central to payer acceptance of both study types is methodologic rigor and transparency, along with tools for the evaluation of the studies that create a reasonably level playing field on which the evidence can be presented and assessed. Since our plenary session panel discussion on ways to improve the value of modeling to payers and providers was held at the ISPOR Annual International Meeting in May 2010, the dialogue between drug manufacturers' health outcomes departments and payers has improved both qualitatively and quantitatively. We hope that this trend continues and produces pharmacoeconomic models that are more useful to decision makers." @default.
• W2075388082 created "2016-06-24" @default.
• W2075388082 creator A5025789949 @default.
• W2075388082 date "2012-09-01" @default.
• W2075388082 modified "2023-10-17" @default.
• W2075388082 title "Creating Models That Meet Decision Makers' Needs: A US Payer Perspective" @default.
• W2075388082 cites W1965870971 @default.
• W2075388082 cites W2054913380 @default.
• W2075388082 cites W2134023402 @default.
• W2075388082 cites W2150712216 @default.
• W2075388082 cites W2170239385 @default.
• W2075388082 doi "https://doi.org/10.1016/j.jval.2012.03.1386" @default.
• W2075388082 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22999126" @default.
• W2075388082 hasPublicationYear "2012" @default.
• W2075388082 type Work @default.
• W2075388082 sameAs 2075388082 @default.
• W2075388082 citedByCount "3" @default.
• W2075388082 countsByYear W20753880822013 @default.
• W2075388082 countsByYear W20753880822015 @default.
• W2075388082 crossrefType "journal-article" @default.
• W2075388082 hasAuthorship W2075388082A5025789949 @default.
• W2075388082 hasBestOaLocation W20753880821 @default.
• W2075388082 hasConcept C12713177 @default.
• W2075388082 hasConcept C144133560 @default.
• W2075388082 hasConcept C154945302 @default.
• W2075388082 hasConcept C162324750 @default.
• W2075388082 hasConcept C41008148 @default.
• W2075388082 hasConcept C539667460 @default.
• W2075388082 hasConceptScore W2075388082C12713177 @default.
• W2075388082 hasConceptScore W2075388082C144133560 @default.
• W2075388082 hasConceptScore W2075388082C154945302 @default.
• W2075388082 hasConceptScore W2075388082C162324750 @default.
• W2075388082 hasConceptScore W2075388082C41008148 @default.
• W2075388082 hasConceptScore W2075388082C539667460 @default.
• W2075388082 hasIssue "6" @default.
• W2075388082 hasLocation W20753880821 @default.
• W2075388082 hasLocation W20753880822 @default.
• W2075388082 hasOpenAccess W2075388082 @default.
• W2075388082 hasPrimaryLocation W20753880821 @default.
• W2075388082 hasRelatedWork W1524186701 @default.
• W2075388082 hasRelatedWork W1533755975 @default.
• W2075388082 hasRelatedWork W2067516908 @default.
• W2075388082 hasRelatedWork W2068873346 @default.
• W2075388082 hasRelatedWork W2105401577 @default.
• W2075388082 hasRelatedWork W2373031741 @default.
• W2075388082 hasRelatedWork W2405358605 @default.
• W2075388082 hasRelatedWork W2932643039 @default.
• W2075388082 hasRelatedWork W3123458564 @default.
• W2075388082 hasRelatedWork W798851750 @default.
• W2075388082 hasVolume "15" @default.
• W2075388082 isParatext "false" @default.
• W2075388082 isRetracted "false" @default.
• W2075388082 magId "2075388082" @default.
• W2075388082 workType "article" @default.