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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.

Abstract:
This paper demonstrates the value of practicality of applying an iterative framework for managing the dynamic process of health technology assessment. The framework uses Bayesian statistical decision theory and value of information (VOI) analysis to inform decision-making regarding appropriate patient management and to direct future research effort over the lifetime of a technology. Within the paper, the framework is applied to a policy decision regarding pre-operative patient management before major elective surgery, for which trial data is available. The evidence available prior to the trial is used to determine the appropriate method of patient management and to ascertain whether, at the time of commissioning, the trial was potentially worthwhile. The prior information is then updated with the trial data via a Bayesian analysis using informative priors. This post-trial information set is then used to re-assess the appropriate method for patient management and to determine whether there is a requirement for any further research.

Abstract:
In a budget constrained healthcare system the decision to invest in implementation strategies must be made alongside decisions regarding investment in healthcare services and further research. We present a single, unified framework that simultaneously addresses the problem of allocating funds between these separate but linked activities.

The framework presents a simple 4 state world where both information and implementation can be either at the current level or 'perfect'. Through this framework it is possible to determine the maximum return to further research and an upper bound on the value of adopting implementation strategies. The framework is demonstrated through a case study of a health care technology previously considered by the UK National Institute for Health and Clinical Excellence (NICE).

The case study identifies several key factors that influence the expected values of perfect information and perfect implementation. These factors include the maximum acceptable cost-effectiveness ratio, the level of uncertainty surrounding the adoption decision, the expected net benefits associated with the technologies, the current level of implementation and the size of the eligible population.