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Beyond the cost-effectiveness acceptability curve: The appropriateness of rank probabilities for presenting the results of economic evaluation in multiple technology appraisal.

ABSTRACT: The cost-effectiveness acceptability curve (CEAC) shows the probability that an option ranks first for net benefit. Where more than two options are under consideration, the CEAC offers only a partial picture of the decision uncertainty. This paper discusses the appropriateness of showing the full set of rank probabilities for reporting the results of economic evaluation in multiple technology appraisal (MTA). A case study is used to illustrate the calculation of rank probabilities and associated metrics, based on Monte Carlo simulations from a decision model. Rank probabilities are often used to show uncertainty in the results of network meta-analysis, but until now have not been used for economic evaluation. They may be useful decision-making tools to complement the CEAC in specific MTA contexts.

SUBMITTER: Epstein D

PROVIDER: S-EPMC6790661 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Beyond the cost-effectiveness acceptability curve: The appropriateness of rank probabilities for presenting the results of economic evaluation in multiple technology appraisal.

Epstein David D

Health economics 20190502 6

The cost-effectiveness acceptability curve (CEAC) shows the probability that an option ranks first for net benefit. Where more than two options are under consideration, the CEAC offers only a partial picture of the decision uncertainty. This paper discusses the appropriateness of showing the full set of rank probabilities for reporting the results of economic evaluation in multiple technology appraisal (MTA). A case study is used to illustrate the calculation of rank probabilities and associated ...[more]

PMID: 31050043

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Project description:BACKGROUND:Perinatal depression is a prevalent public health concern. Although preventive interventions exist, there is limited literature on the acceptability and appropriateness of these interventions, especially those delivered by paraprofessionals. The Mothers and Babies Program (MB) is a group-based perinatal depression preventive intervention delivered prenatally. A cluster-randomized controlled trial examined the acceptability, appropriateness, and effectiveness of MB delivered by mental health professionals compared to paraprofessional staff from home visiting programs. METHODS:The full study enrolled 874 pregnant women. Fifty-three facilitators were trained and delivered the MB intervention to women in one of seven states in the United States. Semi-structured interviews were attempted with a randomly-selected subset of the full sample of pregnant women who received the MB intervention and with all facilitators. Specifically, interviews were conducted with 88 women who received the MB group intervention (45 in the paraprofessional-led arm and 43 in the mental health professional-led arm) and 46 women who facilitated the groups (27 home visiting staff and 19 mental health professionals). Interviews were conducted over the phone in English or Spanish and audio recorded. The recordings were translated into English, as needed, and transcribed. Thematic analysis was conducted using NVIVO to identify key themes related to intervention acceptability and appropriateness. Similarities and differences between study arms were explored. RESULTS:Clients and facilitators found the MB content and group format acceptable. Challenges included maintaining group attendance, transportation issues, and managing group discussion. Overall, facilitators found the intervention appropriate for pregnant clients with some challenges presented for clients in crisis situations, experiencing housing instability, and with literacy and learning challenges. Participants provided suggestions for improvement, both for the course content and implementation. There were no significant differences found between study arms. CONCLUSIONS:Overall, clients and facilitators enjoyed MB irrespective of study arm, and facilitators found the intervention appropriate for the population. These findings add to the qualitative literature on perinatal depression preventive interventions, specifically those delivered by paraprofessionals. TRIAL REGISTRATION:This trial is registered on ClinicalTrials.gov (Initial post: December 1, 2016; identifier: NCT02979444).

Project description:Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to-or even incompatible with-the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions, which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions.

Dataset Information

Beyond the cost-effectiveness acceptability curve: The appropriateness of rank probabilities for presenting the results of economic evaluation in multiple technology appraisal.

Publications

Beyond the cost-effectiveness acceptability curve: The appropriateness of rank probabilities for presenting the results of economic evaluation in multiple technology appraisal.

Similar Datasets

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