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Acceptability Curves (acceptability + curve)
Selected AbstractsA Bayesian model averaging approach for cost-effectiveness analysesHEALTH ECONOMICS, Issue 7 2009Caterina Conigliani Abstract We consider the problem of assessing new and existing technologies for their cost-effectiveness in the case where data on both costs and effects are available from a clinical trial, and we address it by means of the cost-effectiveness acceptability curve. The main difficulty in these analyses is that cost data usually exhibit highly skew and heavy-tailed distributions so that it can be extremely difficult to produce realistic probabilistic models for the underlying population distribution, and in particular to model accurately the tail of the distribution, which is highly influential in estimating the population mean. Here, in order to integrate the uncertainty about the model into the analysis of cost data and into cost-effectiveness analyses, we consider an approach based on Bayesian model averaging: instead of choosing a single parametric model, we specify a set of plausible models for costs and estimate the mean cost with a weighted mean of its posterior expectations under each model, with weights given by the posterior model probabilities. The results are compared with those obtained with a semi-parametric approach that does not require any assumption about the distribution of costs. Copyright © 2008 John Wiley & Sons, Ltd. [source] Patient-centred and professional-directed implementation strategies for diabetes guidelines: a cluster-randomized trial-based cost-effectiveness analysisDIABETIC MEDICINE, Issue 2 2006R. F. Dijkstra Abstract Aims Economic evaluations of diabetes interventions do not usually include analyses on effects and cost of implementation strategies. This leads to optimistic cost-effectiveness estimates. This study reports empirical findings on the cost-effectiveness of two implementation strategies compared with usual hospital outpatient care. It includes both patient-related and intervention-related cost. Patients and methods In a clustered-randomized controlled trial design, 13 Dutch general hospitals were randomly assigned to a control group, a professional-directed or a patient-centred implementation programme. Professionals received feedback on baseline data, education and reminders. Patients in the patient-centred group received education and diabetes passports. A validated probabilistic Dutch diabetes model and the UKPDS risk engine are used to compute lifetime disease outcomes and cost in the three groups, including uncertainties. Results Glycated haemoglobin (HbA1c) at 1 year (the measure used to predict diabetes outcome changes over a lifetime) decreased by 0.2% in the professional-change group and by 0.3% in the patient-centred group, while it increased by 0.2% in the control group. Costs of primary implementation were < 5 Euro per head in both groups, but average lifetime costs of improved care and longer life expectancy rose by 9389 Euro and 9620 Euro, respectively. Life expectancy improved by 0.34 and 0.63 years, and quality-adjusted life years (QALY) by 0.29 and 0.59. Accordingly, the incremental cost per QALY was 32 218 Euro for professional-change care and 16 353 for patient-centred care compared with control, and 881 Euro for patient-centred vs. professional-change care. Uncertainties are presented in acceptability curves: above 65 Euro per annum the patient-directed strategy is most likely the optimum choice. Conclusion Both guideline implementation strategies in secondary care are cost-effective compared with current care, by Dutch standards, for these patients. Additional annual costs per patient using patient passports are low. This analysis supports patient involvement in diabetes in the Netherlands, and probably also in other Western European settings. [source] Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluationsHEALTH ECONOMICS, Issue 12 2005Richard M. Nixon Abstract Background: Overall assessments of cost,effectiveness are now commonplace in informing medical policy decision making. It is often important, however, also to investigate how cost,effectiveness varies between patient subgroups. Yet such analyses are rarely undertaken, because appropriate methods have not been sufficiently developed. Methods: We propose a coherent set of Bayesian methods to extend cost,effectiveness analyses to adjust for baseline covariates, to investigate differences between subgroups, and to allow for differences between centres in a multicentre study using a hierarchical model. These methods consider costs and effects jointly, and allow for the typically skewed distribution of cost data. The results are presented as inferences on the cost,effectiveness plane, and as cost,effectiveness acceptability curves. Results: In applying these methods to a randomised trial of case management of psychotic patients, we show that overall cost,effectiveness can be affected by ignoring the skewness of cost data, but that it may be difficult to gain substantial precision by adjusting for baseline covariates. While analyses of overall cost,effectiveness can mask important subgroup differences, crude differences between centres may provide an unrealistic indication of the true differences between them. Conclusions: The methods developed allow a flexible choice for the distributions used for cost data, and have a wide range of applicability , to both randomised trials and observational studies. Experience needs to be gained in applying these methods in practice, and using their results in decision making. Copyright © 2005 John Wiley & Sons, Ltd. [source] Stochastic league tables: an application to diabetes interventions in the NetherlandsHEALTH ECONOMICS, Issue 5 2005Raymond C. W. Hutubessy Abstract The aim of this paper is to discuss the use of stochastic league tables approach in cost-effectiveness analysis of diabetes interventions. It addresses the common grounds and differences with other methods of presenting uncertainty to decision-makers. This comparison uses the cost-effectiveness results of medical guidelines for Dutch diabetes type 2 patients in primary and secondary care. Stochastic league tables define the optimum expansion pathway as compared to baseline, starting with the least costly and most cost-effective intervention mix. Multi-intervention cost-effectiveness acceptability curves are used as a way to represent uncertainty information on the cost-effectiveness of single interventions as compared to a single alternative. The stochastic league table for diabetes interventions shows that in case of low budgets treatment of secondary care patients is the most likely optimum choice. Current care options of diabetes complications are shown to be inefficient compared to guidelines treatment. With more resources available one may implement all guidelines and improve efficiency. The stochastic league table approach and multi-intervention cost-effectiveness acceptability curves in uncertainty analysis lead to similar results. In addition, the stochastic league table approach provides policy makers with information on affordability by budget level. It fulfils more adequately the information requirements to choose between interventions, using the efficiency criterion. Copyright © 2004 John Wiley & Sons, Ltd. [source] Cost-effectiveness acceptability curves , facts, fallacies and frequently asked questionsHEALTH ECONOMICS, Issue 5 2004Elisabeth Fenwick Abstract Cost-effectiveness acceptability curves (CEACs) have been widely adopted as a method to quantify and graphically represent uncertainty in economic evaluation studies of health-care technologies. However, there remain some common fallacies regarding the nature and shape of CEACs that largely result from the ,textbook' illustration of the CEAC. This ,textbook' CEAC shows a smooth curve starting at probability 0, with an asymptote to 1 for higher money values of the health outcome (,). But this familiar ,ogive' shape which makes the ,textbook' CEAC look like a cumulative distribution function is just one special case of the CEAC. The reality is that the CEAC can take many shapes and turns because it is a graphic transformation from the cost-effectiveness plane, where the joint density of incremental costs and effects may ,straddle' quadrants with attendant discontinuities and asymptotes. In fact CEACs: (i) do not have to cut the y -axis at 0; (ii) do not have to asymptote to 1; (iii) are not always monotonically increasing in ,; and (iv) do not represent cumulative distribution functions (cdfs). Within this paper we present a ,gallery' of CEACs in order to identify the fallacies and illustrate the facts surrounding the CEAC. The aim of the paper is to serve as a reference tool to accompany the increased use of CEACs within major medical journals. Copyright © 2004 John Wiley & Sons, Ltd. [source] The impact of prognosis without treatment on doctors' and patients' resource allocation decisions and its relevance to new drug recommendation processesBRITISH JOURNAL OF CLINICAL PHARMACOLOGY, Issue 2 2008D. Ross Camidge What is already known about this subject ,,The dominant health economic units upon which new treatment funding decisions are made are the incremental cost per life year gained (LYG) or the cost per quality-adjusted life year (QALY) gained. ,,Neither of these units modifies the amount of health gained, by the amount of health patients would have had if they had not been given the treatment under consideration, which may unfairly undervalue the treatments for poor prognosis conditions. ,,How certain patients make decisions about their own treatment has previously been explored, but not how they, or doctors, would allocate hypothetical resource within a healthcare system given information on disease-treatment scenarios' prognoses with and without treatment. What this study adds ,,Information on prognosis without treatment is used within the resource allocation strategies of many doctors and most patients. ,,Individuals use this information in a variety of different ways and a single dominant strategy for quantitative modification of health units is not apparent. ,,Information on prognosis without treatment, or prognosis with standard treatment, is available from the control arm of randomized controlled clinical trials and should be used qualitatively to facilitate decision-making around the second inflexion point on cost per QALY/LYG acceptability curves. Aims Health economic assessments increasingly contribute to funding decisions on new treatments. Treatments for many poor prognosis conditions perform badly in such assessments because of high costs and modest effects on survival. We aimed to determine whether underlying shortness of prognosis should also be considered as a modifier in such assessments. Methods Two hundred and eighty-three doctors and 201 oncology patients were asked to allocate treatment resource between hypothetical patients with unspecified life-shortening diseases. The prognoses with and without treatment were varied such that consistent use of one of four potential allocation strategies could be deduced: life years gained (LYGs) , which did not incorporate prognosis without treatment information; percentage increase in life years (PILY); life expectancy with treatment (LEWT) or immediate risk of death (IRD). Results Random choices were rare; 47% and 64% of doctors and patients, respectively, used prognosis without treatment in their strategies; while 50% and 32%, respectively, used pure LYG-based strategies. Ranking orders were LYG > PILY > IRD > LEWT (doctors) and LEWT > LYG > IRD > PILY (patients). When LYG information alone could not be used, 76% of doctors prioritized shorter prognoses, compared with 45% of patients. Conclusions Information on prognosis without treatment is used within the resource allocation strategies of many doctors and most patients, and should be considered as a qualitative modifier during the health economic assessments of new treatments for life-shortening diseases. A single dominant strategy incorporating this information for any quantitative modification of health units is not apparent. [source] | ||||||||||