Evidence map›Paper›PMID 17887803›Full record

ArticlePharmacoEconomics2007

Lessons from trial-based cost-effectiveness analyses of mental health interventions: why uncertainty about the outcome, estimate and willingness to pay matters.

Jeffrey S Hoch, Carolyn S Dewa

Abstract read
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In one paragraph

Article in PharmacoEconomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  18. Mental health economics: the Netherlands experience.Global & regional health technology assessment
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jeffrey S HochCentre for Research on Inner City Health, The Keenan Research Centre in Li Ka Shing Knowledge Institute of St. Michaels Hospital, Toronto, Ontario, Canada.
Carolyn S Dewa

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The principal aim of this article is to share lessons learned by the authors while conducting economic evaluations, using clinical trial data, of mental health interventions. These lessons are quite general and have clear relevance for pharmacoeconomic studies. In addition, we explore how net benefit regression can be used to enhance consideration of key issues when conducting an economic evaluation based on clinical trial data. The first study we discuss found that cost-effectiveness results varied markedly based on the choice of both the patient outcome and the willingness to pay for more of that outcome. The importance of willingness to pay was also highlighted in the results from the second study. Even with a set willingness-to-pay value, most of the time the probability that the new treatment was cost effective was not 100%. In the third study, the cost effectiveness of the new treatment varied by patient characteristics. These observations have important implications for pharmacoeconomic studies. Namely, analysts must carefully consider choice of patient outcome, willingness to pay, patient heterogeneity and the statistical uncertainty inherent in the data. Net benefit regression is a useful technique for exploring these crucial issues when undertaking an economic evaluation using patient-level data on both costs and effects.

Indexed as

Clinical Trials as TopicEconomics, PharmaceuticalAlgorithmsCommunity Mental Health ServicesCost-Benefit AnalysisCrisis InterventionHumansMental DisordersQuality-Adjusted Life YearsRegression AnalysisRehabilitation, VocationalResidential TreatmentTreatment Outcome

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.