Expected value of information and decision making in HTA

Eckermann, Simon; Willan, Andrew R.

doi:10.1002/hec.1161

Cited by 157 publications

(184 citation statements)

References 8 publications

Supporting

Mentioning

181

Contrasting

Unclassified

Order By: Relevance

“…Recent articles about VOI methodology have tested the impact of other parameters and assumptions not considered in this study. [5][6][7]15 Our results can be used to help plan RCTs in other rare diseases with high treatment costs. As an example, the perpatient annual cost of enzyme replacement in Gaucher disease with imiglucerase is 70,100, with agalsidase in Fabry disease it is 109,600, and with laronidase in mucopolysaccharidosis 1, it is approximately 311,000.…”

Section: Discussionmentioning

confidence: 91%

“…Grounded in decision theory, VOI methods can be used to assess the uncertainty associated with the existing evidence in the comparison of two competing interventions and identify optimal sample sizes if future research is still needed. [5][6][7][8][9][10][11][12][13][14][15][16] The methods allow determination of the expected value of conducting additional research (e.g., a clinical trial) in monetary terms. The value of additional research, referred to as the expected value of sample information (EVSI), is the amount by which it reduces the expected opportunity loss associated with making decisions in the face of uncertainty.…”

Section: S767mentioning

confidence: 99%

See 1 more Smart Citation

Using Value-of-Information Methods when the Disease Is Rare and the Treatment Is Expensive—The Example of Hemophilia A

et al. 2014

Self Cite

View full text Add to dashboard Cite

BACKGROUND: Hemophilia A is a rare, sex-linked genetic disorder treated with intravenous administration of factor VIII (FVIII) to prevent bleeding; however, approaches vary across and within countries. Value-ofinformation (VOI) methods identify situations in which the cost-benefit evidence is sufficient to adopt one treatment strategy over another; when the evidence is insufficient, VOI methods provide the optimal sample size for additional research. OBJECTIVE: The objective of the study was to use VOI methods in a cost-benefit decision context to evaluate the current evidence in support of using (1) alternate day prophylaxis (AP), (2) tailored prophylaxis (TP) or (3) on-demand treatment (OD) with FVIII to prevent arthropathy in children with severe hemophilia A. METHODS: To apply VOI methods, several parameters such as incidence, time horizon for the decision, costs, and threshold values to avoid MRI-detected joint damage or arthropathy were defined. Two baseline threshold values of willingness to pay for avoiding arthropathy-$200,000 and $400,000-were selected for comparing the treatment strategies. RESULTS: For threshold values<$200,000, OD had a higher expected net benefit than either prophylaxis strategy, and the evidence was sufficient for its adoption. For threshold values > $400,0,00 prophylaxis strategies had higher expected net benefit; however, a new trial with 38 patients per arm was needed to compare AP and TP, yielding an expected net gain of over $17 million. In sensitivity analyses, the results were robust to assumptions regarding discount rate, trial fixed and variable costs, enrollment fraction, and the time horizon. CONCLUSIONS: In rare diseases, evidence is often scarce and insufficient for decision making. In considering the funding of new research and patient reimbursement in rare diseases, VOI methodology may provide more relevant determinations of the value and costs of additional research, compared to standard frequentist methods.KEY WORDS: expected value of sample information; hemophilia; children; prophylaxis; costs.

show abstract

Section: Discussionmentioning

confidence: 91%

Section: S767mentioning

confidence: 99%

Using Value-of-Information Methods when the Disease Is Rare and the Treatment Is Expensive—The Example of Hemophilia A

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…Whilst conceptually separate, they are not independent of one another as (i) if the adoption decision is delayed whilst new research is underway, there will be an opportunity cost to those who could have benefited if the technology does indeed have a positive INB [30] (and vice versa: if the technology actually has a negative INB and it is adopted with a review of the decision following further research then patients would have been better off with the old treatment); and (ii) if there are considerable costs associated with reversing a decision [17]; for example, retraining of staff or costly conversion of facilities to other uses ( [31] cited in [17]). …”

Section: Discussionmentioning

confidence: 99%

“…The principles were first adapted to the healthcare field by Thompson [15], with substantial development undertaken by, among others, Claxton, Briggs, Willan and Eckermann [16][17][18]. VoI is probably most usefully considered as a step in the iterative approach to decision making and research [19][20][21][22][23].…”

Section: Application To Decision Making In the Healthcare Fieldmentioning

confidence: 99%

A Practical Guide to Value of Information Analysis

Wilson

2014

PharmacoEconomics

127

View full text Add to dashboard Cite

Value of information analysis is a quantitative method to estimate the return on investment in proposed research projects. It can be used in a number of ways. Funders of research may find it useful to rank projects in terms of the expected return on investment from a variety of competing projects. Alternatively, trialists can use the principles to identify the efficient sample size of a proposed study as an alternative to traditional power calculations, and finally, a value of information analysis can be conducted alongside an economic evaluation as a quantitative adjunct to the 'future research' or 'next steps' section of a study write up. The purpose of this paper is to present a brief introduction to the methods, a step-by-step guide to calculation and a discussion of issues that arise in their application to healthcare decision making. Worked examples are provided in the accompanying online appendices as Microsoft Excel spreadsheets.

show abstract

“…The impact of costs of reversal [6,[20][21][22] associated with investment in new technology also explains the following:…”

Section: Deeper Implications Of Costs Of Reversalmentioning

confidence: 99%

Kinky Thresholds Revisited: Opportunity Costs Differ in the NE and SW Quadrants

Eckermann

2014

Appl Health Econ Health Policy

Self Cite

View full text Add to dashboard Cite

Historically, a kinked threshold line on the cost-effectiveness plane at the origin was suggested due to differences in willingness to pay (WTP) for health gain with trade-offs in the north-east (NE) quadrant versus willingness to accept (WTA) cost reductions for health loss with trade-offs in the south-west (SW) quadrant. Empirically, WTA is greater than WTP for equivalent units of health, a finding supported by loss aversion under prospect theory. More recently, appropriate threshold values for health effects have been shown to require an endogenous consideration of the opportunity cost of alternative actions in budget-constrained health systems, but also allocative and displacement inefficiency observed in health system practice. Allocative and displacement inefficiency arise in health systems where the least cost-effective program in contraction has a higher incremental cost-effectiveness ratio (ICER = m) than the most cost-effective program in expansion (ICER = n) and displaced services (ICER = d), respectively. The health shadow price derived by Pekarsky, (1n+1d−1m)−1, reflects the opportunity cost of best alternative adoption and financing actions in reimbursing new technology with expected incremental costs and net effect allowing for allocative (n < m), and displacement, inefficiency (d < m). This provides an appropriate threshold value for the NE quadrant. In this paper, I show that for trade-offs in the SW quadrant, where new strategies have lower expected net cost while lower expected net effect than current practice, the opportunity cost is contraction of the least costeffective program, with threshold ICER m. That is, in the SW quadrant, the cost reduction per unit of decreased effect should be compared with the appropriate opportunity cost, best alternative generation of funding. Consequently, appropriate consideration of opportunity cost produces a kink in the threshold at the origin, with the health shadow price in the NE quadrant and ICER of the least cost-effective program in contraction (m) in the SW quadrant having the same general shape as that previously suggested by WTP versus WTA. The extent of this kink depends on the degree of allocative and displacement inefficiency, with no kink in the threshold line strictly only appropriate with complete allocative and displacement efficiency, that is n = d = m. Publication DetailsS. Eckermann, "Kinky thresholds revisited: opportunity costs differ in the NE and SW quadrants", Applied Health Economics and Health Policy 13 1 (2015) Abstract: Historically, a kinked threshold line on the cost-effectiveness plane at the origin was suggested due to differences in willingness to pay (WTP) for health gain with tradeoffs in the north-east (NE) quadrant versus willingness to accept (WTA) cost reductions for health loss with trade-offs in the south-west (SW) quadrant. Empirically, WTA is greater than WTP for equivalent units of health, a finding supported by loss aversion under prospect theory. More recently, appropriate threshold values for health e...

show abstract

Expected value of information and decision making in HTA

Cited by 157 publications

References 8 publications

Using Value-of-Information Methods when the Disease Is Rare and the Treatment Is Expensive—The Example of Hemophilia A

Using Value-of-Information Methods when the Disease Is Rare and the Treatment Is Expensive—The Example of Hemophilia A

A Practical Guide to Value of Information Analysis

Kinky Thresholds Revisited: Opportunity Costs Differ in the NE and SW Quadrants

Contact Info

Product

Resources

About