2018
DOI: 10.1016/j.jval.2018.01.019
|View full text |Cite
|
Sign up to set email alerts
|

Experiences of Structured Elicitation for Model-Based Cost-Effectiveness Analyses

Abstract: BackgroundEmpirical evidence supporting the cost-effectiveness estimates of particular health care technologies may be limited, or it may even be missing entirely. In these situations, additional information, often in the form of expert judgments, is needed to reach a decision. There are formal methods to quantify experts’ beliefs, termed as structured expert elicitation (SEE), but only limited research is available in support of methodological choices. Perhaps as a consequence, the use of SEE in the context o… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
67
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
8

Relationship

2
6

Authors

Journals

citations
Cited by 37 publications
(67 citation statements)
references
References 29 publications
(217 reference statements)
0
67
0
Order By: Relevance
“…Clearly, linking the estimated effects on mortality to QALYs requires a number of assumptions to be made. For a more detailed summary of all assumptions, their justification, and a discussion of their likely impact on the central estimate of the cost per QALY, see Table 32 in Claxton et al 5(p 83) Recently, the plausibility of these assumptions has been examined through structured elicitation from clinical experts, 14 and this work suggests that these assumptions are likely to be conservative with respect to the QALY effects of changes in expenditure (ie, the cost per QALY is likely to be lower than that estimated using these assumptions).…”
Section: Translating Mortality Effects Into Quality-adjusted Life-yearsmentioning
confidence: 99%
“…Clearly, linking the estimated effects on mortality to QALYs requires a number of assumptions to be made. For a more detailed summary of all assumptions, their justification, and a discussion of their likely impact on the central estimate of the cost per QALY, see Table 32 in Claxton et al 5(p 83) Recently, the plausibility of these assumptions has been examined through structured elicitation from clinical experts, 14 and this work suggests that these assumptions are likely to be conservative with respect to the QALY effects of changes in expenditure (ie, the cost per QALY is likely to be lower than that estimated using these assumptions).…”
Section: Translating Mortality Effects Into Quality-adjusted Life-yearsmentioning
confidence: 99%
“…We demonstrate the applicability of the elicitation exercise in practice. Its design draws from wider experience of elicitation in health technology assessment 14 and literature from other areas of science (for example, refs. 15 and 16).…”
mentioning
confidence: 99%
“…Several elicitation procedures are available to obtain information from experts and make a probabilistic representation of their knowledge. Two different approaches of elicitation are commonly used in the literature of structured elicitations for cost-effectiveness analyses: 1) the fixed interval method, in which the expert reports his/her probability of the uncertain quantity of interest θ, for example the recurrence rate, the mortality rate or the time to death, lying in specified intervals, and 2) the variable interval method, in which he/she makes quantile judgements[ 80 ]. Among the common fixed interval elicitation methods, is the trial roulette method, also called the ‘chips and bins method’, where the expert provides probabilities of θ lying in a particular ‘bin’ by allocating ‘chips’ to that bin [ 81 , 82 ].…”
Section: Eliciting Expert Opinionmentioning
confidence: 99%
“… Two different approaches of structured elicitation are recommended for cost-effectiveness analyses: 1) the fixed interval method, in which the expert reports his/her probability of the uncertain quantity of interest θ, for example the recurrence rate, the mortality rate or the time to death, lying in specified intervals, and 2) the variable interval method, in which (s)he makes quantile judgements. Panel**, Grigore 2016[ 79 ], Soares 2018[ 80 ] → Extrapolation methods Standard modelling techniques used in economic evaluation, such as Markov models and Discrete Event Simulation, will likely be appropriate for GRT. The challenge will be to find appropriate data, such as transition probabilities, to populate these models.…”
Section: Affordability and New Payment Modelsmentioning
confidence: 99%