Siebert U.,UMIT University for Health Sciences, Medical Informatics and Technology |
Siebert U.,Center for Personalized Cancer Medicine |
Siebert U.,Center for Health Decision Science |
Siebert U.,Harvard University |
And 3 more authors.
Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen | Year: 2013
Decision analysis (DA) and value-of-information (VOI) analysis provide a systematic, quantitative methodological framework that explicitly considers the uncertainty surrounding the currently available evidence to guide healthcare decisions. In medical decision making under uncertainty, there are two fundamental questions: 1) What decision should be made now given the best available evidence (and its uncertainty)?; 2) Subsequent to the current decision and given the magnitude of the remaining uncertainty, should we gather further evidence (i.e., perform additional studies), and if yes, which studies should be undertaken (e.g., efficacy, side effects, quality of life, costs), and what sample sizes are needed? Using the currently best available evidence, VoI analysis focuses on the likelihood of making a wrong decision if the new intervention is adopted. The value of performing further studies and gathering additional evidence is based on the extent to which the additional information will reduce this uncertainty. A quantitative framework allows for the valuation of the additional information that is generated by further research, and considers the decision maker's objectives and resource constraints. Claxton et al. summarise: "Value of information analysis can be used to inform a range of policy questions including whether a new technology should be approved based on existing evidence, whether it should be approved but additional research conducted or whether approval should be withheld until the additional evidence becomes available." [Claxton K. Value of information entry in Encyclopaedia of Health Economics, Elsevier, forthcoming 2014.] The purpose of this tutorial is to introduce the framework of systematic VoI analysis to guide further research. In our tutorial article, we explain the theoretical foundations and practical methods of decision analysis and value-of-information analysis. To illustrate, we use a simple case example of a foot ulcer (e.g., with diabetes) as well as key references from the literature, including examples for the use of the decision-analytic VoI framework by health technology assessment agencies to guide further research. These concepts may guide stakeholders involved or interested in how to determine whether or not and, if so, which additional evidence is needed to make decisions.
Challenges in the development and reimbursement of personalized medicine-payer and manufacturer perspectives and implications for health economics and outcomes research: A report of the ISPOR personalized medicine special interest group
Faulkner E.,Quintiles |
Faulkner E.,University of North Carolina at Chapel Hill |
Annemans L.,Ghent University |
Garrison L.,University of Washington |
And 13 more authors.
Value in Health | Year: 2012
Background: Personalized medicine technologies can improve individual health by delivering the right dose of the right drug to the right patient at the right time but create challenges in deciding which technologies offer sufficient value to justify widespread diffusion. Personalized medicine technologies, however, do not neatly fit into existing health technology assessment and reimbursement processes. Objectives: In this article, the Personalized Medicine Special Interest Group of the International Society for Pharmacoeconomics and Outcomes Research evaluated key development and reimbursement considerations from the payer and manufacturer perspectives. Methods: Five key areas in which health economics and outcomes research best practices could be developed to improve value assessment, reimbursement, and patient access decisions for personalized medicine have been identified. Results: These areas are as follows: 1 research prioritization and early value assessment, 2 best practices for clinical evidence development, 3 best practices for health economic assessment, 4 addressing health technology assessment challenges, and 5 new incentive and reimbursement approaches for personalized medicine. Conclusions: Key gaps in health economics and outcomes research best practices, decision standards, and value assessment processes are also discussed, along with next steps for evolving health economics and outcomes research practices in personalized medicine. © 2012 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.
Geiger-Gritsch S.,UMIT University for Health Sciences, Medical Informatics and Technology |
Geiger-Gritsch S.,Ludwig Boltzmann Institute of Health Technology Assessment |
Stollenwerk B.,UMIT University for Health Sciences, Medical Informatics and Technology |
Stollenwerk B.,Helmholtz Center for Environmental Research |
And 8 more authors.
Oncologist | Year: 2010
Objective. We performed a meta-analysis on adverse events seen with bevacizumab to combine the existing evidence about its safety in patients with advanced cancer. Methods. A systematic literature search was conducted to identify published, randomized controlled trials of bevacizumab in cancer patients with data on adverse events available. The primary endpoint was "severe adverse event," a composite of grade 3 and 4 adverse events. Secondary endpoints for the exploratory analysis were individual adverse events. We used random-effects meta-analysis to combine data. Results. Thirteen eligible publications were identified and eight trials reported the primary endpoint. Compared with the control group, the bevacizumab group had a slightly higher risk for any severe adverse event (pooled relative risk, 1.10; 95% confidence interval [95% CI], 1.01-1.19). The pooled risk difference was 7%(95%CI, 1%-13%), withanumber neededtoharm of 14 treated patients. Exploratory analyses showed a statistically significant higher risk for eight of the 15 evaluated secondary endpoints: bevacizumab was associated with a fourfold higher risk for hypertension, epistaxis, and gastrointestinal hemorrhage/perforation; a threefold higher risk for any bleeding events; and a lower, but elevated risk for proteinuria, leukopenia, diarrhea, and asthenia. No statistically significant differences were found for any thrombotic event (arterial or venous), hemoptysis, cardiac event, thrombocytopenia, neutropenia, impaired wound healing, or death related to an adverse event. Conclusion. Treatment with bevacizumab was associated with a slightly higher risk for any severe (grade 3 or 4) adverse event in patients with cancer. The result may impact individual benefit-risk assessments and policy guidelines. ©AlphaMed Press.
Scheuringer M.,MSD Sharp and Dohme GmbH |
Sahakyan N.,Ludwig Maximilians University of Munich |
Sahakyan N.,Center for Personalized Cancer Medicine |
Sahakyan N.,UMIT University for Health Sciences, Medical Informatics and Technology |
And 3 more authors.
Cost Effectiveness and Resource Allocation | Year: 2012
Guidance from the Institute for Quality and Efficiency in Health Care (IQWiG) on cost estimation in cost-benefit assessments in Germany acknowledges the need for standardization of costing methodology. The objective of this review was to assess current methods for deriving clinical event costs in German economic evaluations. A systematic literature search of 24 databases (including MEDLINE, BIOSIS, the Cochrane Library and Embase) identified articles, published between January 2005 and October 2009, which reported cost-effectiveness or cost-utility analyses. Studies assessed German patients and evaluated at least one of 11 predefined clinical events relevant to patients with diabetes mellitus. A total of 21 articles, describing 199 clinical cost events, met the inclusion criteria. Year of costing and time horizon were available for 194 (97%) and 163 (82%) cost events, respectively. Cost components were rarely specified (32 [16%]). Costs were generally based on a single literature source (140 [70%]); where multiple sources were cited (32 [16%]), data synthesis methodology was not reported. Cost ranges for common events, assessed using a Markov model with a cycle length of 12 months, were: acute myocardial infarction (nine studies), first year, 4,618-17,556 €; follow-up years, 1,006-3,647 €; and stroke (10 studies), first year; 10,149-24,936 €; follow-up years, 676-7,337 €. These results demonstrate that costs for individual clinical events vary substantially in German health economic evaluations, and that there is a lack of transparency and consistency in the methods used to derive them. The validity and comparability of economic evaluations would be improved by guidance on standardizing costing methodology for individual clinical events. © 2012 Scheuringer et al.; licensee BioMed Central Ltd.
Oberaigner W.,Tyrolean State Hospitals Ltd |
Oberaigner W.,University for Information Science and Technology |
Oberaigner W.,Center for Personalized Cancer Medicine |
Oberaigner W.,TILAK GmbH |
And 4 more authors.
European Journal of Public Health | Year: 2011
Background: Gender aspects in medicine are receiving increasing attention, namely also in oncology. For this reason, we decided to investigate whether for solid cancer sites women have better survival outcome than do men in the population of Tyrol, Austria. Methods: We conducted an observational population-based study in Tyrol. All solid cancer sites excluding non-melanoma skin cancer and sex-specific sites were analysed in total and all specific sites with more than 500 patients in the analysis. By the end of 2006, follow-up was ended. We applied a relative excess risk model, thus correcting for differences in life expectancy between women and men. Results: For all cancer sites combined, after adjusting for case mix, women had a relative excess risk of 0.95 (95 CI 0.91-0.99). For the following sites our analysis resulted in a relative excess risk statistically different from 1, namely for women as compared to men: head and neck without larynx 0.72 (95 CI 0.56-0.93), stomach 0.86 (95 CI 0.75-0.97) and lung 0.82 (95 CI 0.75-0.90). Conclusion: In a healthcare system with free access to diagnostics and therapy, after adjusting for staging distribution female cancer patients have a lesser excess mortality risk than do men for lung, stomach and head and neck cancer and also for all cancer sites combined after adjusting for case mix. © 2010 The Author.