Case study in the Bayesian analysis of a cost‐effectiveness trial in the evaluation of health care technologies: Depression

Stevens, John; O'Hagan, Anthony; Miller, Paul

doi:10.1002/pst.43

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…To show the effect of including covariates we also present the results obtained from the Bayesian models without covariates, proposed by Stevens et al [39], for quantitative and binary effectiveness.…”

Section: Resultsmentioning

confidence: 99%

Bayesian regression models for cost-effectiveness analysis

Polo

Hernández

Badı́a³

et al. 2005

Eur J Health Econ

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Recent studies have shown how cost-effectiveness analysis can be undertaken in a regression framework. This contribution explores the use of practical regression models for estimating cost-effectiveness from a Bayesian perspective. Two different Bayesian models are described. The first considers the outcome measure to be a quantitative variable. In the second model the individual outcome measure is a binary variable with value 1 if any objective has been achieved. We describe the implementation of the model using data from a trial that compares two highly active antiretroviral therapies in HIV asymptomatic patients. Data on direct cost and data effectiveness (percentage of patients with undetectable viral load and quality of life) were recorded. If we consider the quality of life as an effectiveness measure, the new treatment is preferred for a willingness to pay more than Euro 142.3 for an increase in the quality of life. For illustrative purposes, if we compare the results with an analogous model that does not include covariates, the critical value becomes Euro 247.4. For the binary measure of effectiveness the control treatment dominates the new treatment.

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Section: Resultsmentioning

confidence: 99%

Bayesian regression models for cost-effectiveness analysis

Polo

Hernández

Badı́a³

et al. 2005

Eur J Health Econ

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“…The examples to date relate to the elicitation of prior beliefs in clinical trials and survival analysis . Elicited data has also been seldom used in both stochastic cost effectiveness analysis (where the data on net costs and net effects of alternative treatments generated within a trial is updated with prior information on these) and in decision modelling . Although there is increasing awareness of the advantages of using elicitation , the design and conduct of elicitation needs to be further explored to assess its potential feasibility and to facilitate the transference of guidance from the existing elicitation literature (mainly relevant for eliciting parameters of statistical models rather than decision models).…”

Section: Discussionmentioning

confidence: 99%

Methods to elicit experts’ beliefs over uncertain quantities: application to a cost effectiveness transition model of negative pressure wound therapy for severe pressure ulceration

Soares

Bojke

Dumville

et al. 2011

Statistics in Medicine

131

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We can use decision models to estimate cost effectiveness, quantify uncertainty regarding the adoption decision and provide estimates of the value of further research. In many cases, the existence of only limited data with which to populate a decision model can mean that a cost-effectiveness analysis either does not proceed or may misrepresent the degree of uncertainty associated with model inputs. An example is the case of negative pressure wound therapy (NPWT) used to treat severe pressure ulceration, for which the evidence base is limited and sparse. There is, however, substantial practical experience of using this treatment and its comparators. We can capture this knowledge quantitatively to inform a cost-effectiveness model by eliciting beliefs from experts. This paper describes the design and conduct of an elicitation exercise to generate estimates of multiple uncertain model inputs and validate analytical assumptions for a decision model on the use of NPWT. In designing the exercise, the primary focus was the use of elicitation to inform decision models (multistate models), where representations of uncertain beliefs need to be probabilistically coherent. This paper demonstrates that it is feasible to collect formally elicited evidence to inform decision models.

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“…The second outcome (e 2ij ) is continuous, and so we assume it to be normally distributed. We also recognize the skewness in the cost data; thus it is realistic to assume that the cost is log-normally distributed [16,22].…”

Section: Model Speci¢cationmentioning

confidence: 99%

Bayesian cost-effectiveness analysis with two measures of effectiveness: the cost-effectiveness acceptability plane

Hernández

Polo

2006

Health Econ.

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Cost-effectiveness analysis (CEA) compares the costs and outcomes of two or more technologies. However, there is no consensus about which measure of effectiveness should be used in each analysis. Clinical researchers have to select an appropriate outcome for their purpose, and this choice can have dramatic consequences on the conclusions of their analysis. In this paper we present a Bayesian cost-effectiveness framework to carry out CEA when more than one measure is considered. In particular, we analyse the case in which two measures of effectiveness, one binary and the other continuous, are considered. Decision-making measures, such as the incremental cost-effectiveness ratio, incremental net-benefit and cost-effectiveness acceptability curves, are used to compare costs and one measure of outcome. We propose an extension of cost-acceptability curves, namely the cost-effectiveness acceptability plane, as a suitable measure for decision taking. The models were validated using data from two clinical trials. In the first one, we compared four highly active antiretroviral treatments applied to asymptomatic HIV patients. As measures of effectiveness, we considered the percentage of patients with undetectable levels of viral load, and changes in quality of life, measured according to EuroQol. In the second clinical trial we compared three methadone maintenance programmes for opioid-addicted patients. In this case, the measures of effectiveness considered were quality of life, according to the Nottingham Health Profile, and adherence to the treatment, measured as the percentage of patients who participated in the whole treatment programme.

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Case study in the Bayesian analysis of a cost‐effectiveness trial in the evaluation of health care technologies: Depression

Cited by 9 publications

References 30 publications

Bayesian regression models for cost-effectiveness analysis

Bayesian regression models for cost-effectiveness analysis

Methods to elicit experts’ beliefs over uncertain quantities: application to a cost effectiveness transition model of negative pressure wound therapy for severe pressure ulceration

Bayesian cost-effectiveness analysis with two measures of effectiveness: the cost-effectiveness acceptability plane

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