title: When is enough evidence enough? - Using systematic decision analysis and value-of-information analysis to determine the need for further evidence.
abstract: 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.
authors: Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Area Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria; Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA; Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: public-health@umit.at.
authors: Uwe
authors: U
authors: Siebert
authors: Ursula
authors: U
authors: Rochau
authors: Karl
authors: K
authors: Claxton
mesh_terms: D000368:Aged; D035843:Biomedical Research; D003362:Cost-Benefit Analysis; D003625:Data Collection / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D017719:Diabetic Foot / Q000401:mortality / Q000601:surgery; D019317:Evidence-Based Medicine / Q000592:standards*; D018409:Foot Injuries / Q000401:mortality / Q000601:surgery; D005858:Germany; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D018570:Risk Assessment; D016019:Survival Analysis; D035501:Uncertainty; D014946:Wound Infection / Q000401:mortality / Q000601:surgery
keywords: DA; Decision Analysis; EA; ENBS; EVPI; EVPPI; EVSI; Entscheidungsanalyse; Evidenz-basierte Gesundheitsversorgung; Expected Net Benefit of Sampling; Expected Value of Partial Perfect Information; Expected Value of Perfect Information; Expected Value of Sample Information; HTA; Health Technology Assessment; ICER; Incremental Cost-effectiveness Ratio; Incremental Costs; Incremental Effectiveness; LE; LYs; Life Expectancy; Life Years; Medical decision analysis; Medizinische Entscheidungsanalyse; NHB; NHS; NMB; National Health System; Net Health Benefit; Net Monetary Benefit; PSA; Probabilistic Sensitivity Analysis; QALE; QALYs; Quality-adjusted Life Expectancy; Quality-adjusted Life Years; RCT; Randomized Controlled Trial; Value-of-Information; Value-of-Information Analyse; VoI; WTP; Willingness-to Pay; Willingness-to-Pay Threshold; evidence-based healthcare; value- of- information analysis; ΔC; ΔE; λ