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To support decision making, many countries have now introduced some formal assessment process to evaluate whether health technologies represent good 'value for money'. These often take the form of decision models that can be used to explore elements of importance to generalisability of study results across clinical settings and jurisdictions. The objective of this review was to assess whether articles reporting decision-analytic models in the area of osteoporosis provided enough information to enable decision makers in different countries/jurisdictions to fully appreciate the variability of results according to location and be able to apply the evaluation to their own setting. Of the 18 articles included in the review, only three explicitly stated the decision-making audience. It was not possible to infer a decision-making audience in eight studies. The target population was well reported, as were resource and cost data, and clinical data used for estimates of relative risk reduction. However, baseline risk was rarely adapted to the relevant jurisdiction, and when no decision maker was explicit it was difficult to assess whether the reported cost and resource use data were in fact relevant. A few studies used sensitivity analysis to explore elements of generalisability, such as compliance rates and baseline fracture risk rates, although such analyses were generally restricted to evaluating parameter uncertainty. This review found that variability in cost effectiveness across locations is addressed to a varying extent in modelling studies in the field of osteoporosis, limiting their use for decision makers across different locations. Transparency of reporting is expected to increase as methodology develops and decision makers publish 'reference case' type guidance.
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To review, and to develop further, the methods used to assess and to increase the generalisability of economic evaluation studies.
Methodological studies relating to economic evaluation in healthcare were searched. This included electronic searches of a range of databases, including PREMEDLINE, MEDLINE, EMBASE and EconLit, and manual searches of key journals. The case studies of a decision analytic model involved highlighting specific features of previously published economic studies related to generalisability and location-related variability. The case-study involving the secondary analysis of cost-effectiveness analyses was based on the secondary analysis of three economic studies using data from randomised trials.
The factor most frequently cited as generating variability in economic results between locations was the unit costs associated with particular resources. In the context of studies based on the analysis of patient-level data, regression analysis has been advocated as a means of looking at variability in economic results across locations. These methods have generally accepted that some components of resource use and outcomes are exchangeable across locations. Recent studies have also explored, in cost-effectiveness analysis, the use of tests of heterogeneity similar to those used in clinical evaluation in trials. The decision analytic model has been the main means by which cost-effectiveness has been adapted from trial to non-trial locations. Most models have focused on changes to the cost side of the analysis, but it is clear that the effectiveness side may also need to be adapted between locations. There have been weaknesses in some aspects of the reporting in applied cost-effectiveness studies. These may limit decision-makers' ability to judge the relevance of a study to their specific situations. The case study demonstrated the potential value of multilevel modelling (MLM). Where clustering exists by location (e.g. centre or country), MLM can facilitate correct estimates of the uncertainty in cost-effectiveness results, and also a means of estimating location-specific cost-effectiveness. The review of applied economic studies based on decision analytic models showed that few studies were explicit about their target decision-maker(s)/jurisdictions. The studies in the review generally made more effort to ensure that their cost inputs were specific to their target jurisdiction than their effectiveness parameters. Standard sensitivity analysis was the main way of dealing with uncertainty in the models, although few studies looked explicitly at variability between locations. The modelling case study illustrated how effectiveness and cost data can be made location-specific. In particular, on the effectiveness side, the example showed the separation of location-specific baseline events and pooled estimates of relative treatment effect, where the latter are assumed exchangeable across locations.
A large number of factors are mentioned in the literature that might be expected to generate variation in the cost-effectiveness of healthcare interventions across locations. Several papers have demonstrated differences in the volume and cost of resource use between locations, but few studies have looked at variability in outcomes. In applied trial-based cost-effectiveness studies, few studies provide sufficient evidence for decision-makers to establish the relevance or to adjust the results of the study to their location of interest. Very few studies utilised statistical methods formally to assess the variability in results between locations. In applied economic studies based on decision models, most studies either stated their target decision-maker/jurisdiction or provided sufficient information from which this could be inferred. There was a greater tendency to ensure that cost inputs were specific to the target jurisdiction than clinical parameters. Methods to assess generalisability and variability in economic evaluation studies have been discussed extensively in the literature relating to both trial-based and modelling studies. Regression-based methods are likely to offer a systematic approach to quantifying variability in patient-level data. In particular, MLM has the potential to facilitate estimates of cost-effectiveness, which both reflect the variation in costs and outcomes between locations and also enable the consistency of cost-effectiveness estimates between locations to be assessed directly. Decision analytic models will retain an important role in adapting the results of cost-effectiveness studies between locations. Recommendations for further research include: the development of methods of evidence synthesis which model the exchangeability of data across locations and allow for the additional uncertainty in this process; assessment of alternative approaches to specifying multilevel models to the analysis of cost-effectiveness data alongside multilocation randomised trials; identification of a range of appropriate covariates relating to locations (e.g. hospitals) in multilevel models; and further assessment of the role of econometric methods (e.g. selection models) for cost-effectiveness analysis alongside observational datasets, and to increase the generalisability of randomised trials.
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Objective: We present unit costs corresponding to resource information collected in the Schizophrenia Outpatient Health Outcomes (SOHO) Study.Method: The SOHO study is a 3-year, prospective, observational study of health outcomes associated with antipsychotic treatment in out-patients treated for schizophrenia. The study is being conducted across 10 European countries (Denmark, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Spain and the UK) and includes over 10 800 patients and over 1000 investigators. To identify the best available unit costs of hospital admissions, day care and psychiatrist out-patient visits, a tariff-based approach was used.Results: Unit costs were obtained for nine of the 10 countries and were adjusted to 2000 price levels by consumer price indices and converted to US$ using purchasing power parity rates (and on to €).Conclusion: The paper illustrates the need to balance the search for sound unit costs with pragmatic solutions in the costing of international economic evaluations.