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Abstract:
INTRODUCTION: Studies has proven that the microprocessor controlled prosthetic knee, the C-Leg, is associated with several advantages in comparison to the conventional non-microprocessor knee units. However, as the C-Leg is substantially more costly compared to the non-microprocessor controlled alternatives, the potential benefits need to be compared with the increased costs as cost-effectiveness has become a key criteria for decision-makers when deciding which health-care interventions should be made available in collectively funded health-care systems. Although little clinical evidence exists, decision makers still have to make recommendations regarding the provision of C-leg and therefore the purpose of this study was to estimate the costs and health outcomes of C-Leg and non-microprocessor controlled knees employing a decision model based on available evidence and newly collected data on health-related quality of life of patients with C-leg.

METHOD: A decision-analysis Markov model was created. The costs, rate and duration of problems, knee survival and quality-adjusted life-years were obtained from interviews with 20 patients and 5 prosthetist with experience of both C-leg and non microprocessor controlled knees. According to recent guidelines, the model was analyzed using second-order Monte-Carlo simulation in order to reflect uncertainty in the model inputs. The analysis was undertaken from a Swedish health-care perspective. Main outcome measures where cost (in 2006 Euros) per quality-adjusted life-year gained by providing a transfemoral amputee with C-leg as opposed to a non microprocessor controlled knee.

RESULTS: The analysis resulted in a mean incremental cost for C-Leg of 7 657 and 2.38 incremental quality-adjusted life-years gained, yielding a cost per quality-adjusted life-year gained of 3 218.

CONCLUSION: In conclusion, we have argued for the importance of providing decision makers with relevant information on costs and health outcomes of different treatment strategies on actual decision problems even when limited evidence exists. We have provided results where costs and a broadly defined health outcome in term of quality-adjusted life years have been estimated showing that given existing evidence C-leg appear to yield positive health outcomes at an acceptable cost. The utilization of Bayesian expert elicitation to achieve better priors could be of significant interest in future research.

Abstract:
Objective: Quality-adjusted life years, where time spent in a health state is weighted by a quality-adjustment weight representing the health-related quality of life associated with the health state, is a recommended outcome measure in economic evaluations of health care. The quality adjustment should mirror preferences for health states but different views exist to whose preferences should be used for such quality adjustment. The aim of this work was to compare patient's preferences with those of the general public for the same health states.

Method: A sample of 362 patients with unstable coronary artery disease from the Frisc II trial, valued their quality of life in the acute phase and after 3, 6 and 12 months. The EQ-5D questionnaire and the direct Time Trade-off method (TTO) of valuing health were used. A regression technique managing panel data was used in predicting TTO values using the EQ-5D questions as explanatory variables.

Result: Of the total 243 health states in EQ-5D, none were valued higher by the general public. In other words, individuals who experience a certain health condition tend to estimate this condition as less severe compared with a population that hypothetically estimate the same condition. The difference was large for severe health conditions, but rather small for healthy patients.

Conclusions: It appears to be a difference between individual and public preferences for health conditions. Since this difference is nonlinear with respect to different health conditions severity, the normative choice of whose preferences to use in health economic evaluations can have an impact on the result of the evaluation.

Choice of preferences, EQ-5D, QALY, Panel data

Abstract:
Background: Event based models are driven by the occurrence of clinical events such as primary clinical endpoints in randomised trials or adverse events associated with treatment. Statistical analyses of individual-patient data are used to determine event rates and further statistical analyses are performed to estimate survival, costs and health-related quality of life conditional on an event having occurred. For economic evaluations of health-care programmes such an approach has several advantages as extrapolation is facilitated, it is possible to explore cost-effectiveness in different risk groups and it makes it possible to bring in relevant external evidence such as pooled treatment effect. However, event based modelling also poses methodological challenges concerning not only technical issues but also conceptual ones regarding the scientific method. The aim of this paper is to explore and discuss these methodological challenges.

Methods: Published event based models were reviewed and examples from a recently developed event based model in acute coronary syndrome were used to discuss and exemplify several of the methodological issues involving event based modelling.

Results: The event based modelling approach normally uses randomised evidence to determine rates of clinical events, but given that an event has occurred, life expectancy, costs and health-related quality of life are estimated conditional on the event rather than randomised treatment. Some would argue this is appropriate as treatment only affect costs and quality of life through the impact of events rates. However, others would argue that such an approach is inappropriate as it adds 'structure' to the randomised evidence in terms of costs, life-expectancy and quality of life assuming conditional independence. Regarding more technical aspects, several methodological issues need to be considered as relatively advanced statistical models are combined in a decision-analytic framework to determine cost-effectiveness. The most important advantage of event based modelling identified in this work is that it provides a tool to estimate cost-effectiveness in a way relevant for policy, i.e. enabling the estimation of lifetime costs and health outcomes in different subgroups utilising all relevant evidence. Some of the challenges identified in this work include the choice of covariates to be included in the statistical analyses and the presentation of the results and probabilistic sensitivity analyses as much of the inputs into the cost-effectiveness model will be based on risk equations which can potentially define a very large number of subgroups.

Conclusion: Although there are still methodological issues that need addressing in this framework, event based modelling is a useful method for providing relevant cost-effectiveness evidence.