Operationalising the capability approach as an outcome measure in public health: The development of the OCAP-18.

There is growing interest in operationalising the capability approach to measure quality of life. This paper reports the results of a research project undertaken in 2007 that sought to reduce and refine a longer survey in order to provide a summary measure of wellbeing and capability in the realm of public health. The reduction and refinement of the questionnaire took place across a number of stages, using both qualitative (five focus group discussions and 17 in-depth interviews) and quantitative (secondary data analysis, N = 1048 and primary data collection using postal surveys and interviews, N = 45) approaches. The questionnaire was reduced from its original 60+ questions to 24 questions (including demographic questions). Each of Nussbaum's ten Central Human Capabilities are measured using one (or more) of the 18 specific capability items which are included in the questionnaire (referred to as the OCAP-18). Analysis of the questionnaire responses (N = 198) found that respondents differed with respect to the levels of capabilities they reported, and that these capabilities appear to be sensitive to one's gender, age, income and deprivation decile. An index of capability, estimated by assuming equal weight for each capability question, found that the average level of capability amongst respondents was 12.44 (range 3-17.75). This index was found to be highly correlated with a measure of health (EQ-5D) and wellbeing (global QoL), although some differences were apparent. This project operationalised the capability approach to produce an instrument to measure the effectiveness (and cost effectiveness) of public health interventions; the resulting OCAP-18 appears to be responsive and measure something supplementary to health and wellbeing, thus offers a promising addition to the current suite of outcome measures that are available.

Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6.

A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.

Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--6.

A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value of information analysis. The article also makes extensive recommendations around the reporting of uncertainty, in terms of both deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.

Outcome measurement in economic evaluations of public health interventions: a role for the capability approach?

Public health interventions have received increased attention from policy makers, and there has been a corresponding increase in the number of economic evaluations within the domain of public health. However, methods to evaluate public health interventions are less well established than those for medical interventions. Focusing on health as an outcome measure is likely to underestimate the impact of many public health interventions. This paper provides a review of outcome measures in public health; and describes the benefits of using the capability approach as a means to developing an all encompassing outcome measure.

Value of information and value of implementation: application of an analytic framework to inform resource allocation decisions in metastatic hormone-refractory prostate cancer.

OBJECTIVE: In a budget-constrained health-care system, decisions about investing in strategies to promote implementation have to be made alongside decisions about health-care provision and research funding. Using a Bayesian decision-theoretic approach, an analytic framework has been developed to inform these separate but related decisions, establishing the expected value of both perfect information (EVPI) and perfect implementation (EVPIM). We applied this framework to inform decision-making about resource allocation to metastatic hormone-refractory prostate cancer (mHRPC) in the UK. METHODS: Based on available evidence on the cost-effectiveness of all plausible treatments for mHRPC, we determined which treatment option(s) were cost-effective and explored the uncertainty surrounding this decision. Given the decision uncertainty and the variation in care provided by health-care professionals, we then determined the EVPI and EVPIM. Finally, we performed sensitivity analyses to explore the influence of alternative assumptions regarding various decision parameters on the efficiency of resource allocation. RESULTS: Depending on the cost-effectiveness threshold (lambda), we identified mitoxantrone plus prednisone/prednisolone and docetaxel plus prednisone/prednisolone (3 weekly) as the optimal treatments for mHRPC. Given current clinical practice, there appears to be considerable scope for improving the efficiency of health-care provision: the EVPI (estimated to be over pound13 million) indicates that acquiring further information could be cost-effective; and the EVPIM (estimated to be over pound4 million) suggests that investing in strategies to implement the treatments regimens being identified as optimal is potentially worthwhile. Through sensitivity analyses, we found that the EVPI and EVPIM are mainly driven by lambda, the number of treatment options being considered, the current level of implementation, and the size of the eligible patient population. CONCLUSION: The application demonstrates that the framework provides a simple and useful analytic tool for decision-makers to address resource allocation problems between health-care provision, further research, and implementation efforts.

Optimal cost-effectiveness decisions: the role of the cost-effectiveness acceptability curve (CEAC), the cost-effectiveness acceptability frontier (CEAF), and the expected value of perfection information (EVPI).

OBJECTIVE: To demonstrate how the optimal decision and level of uncertainty associated with that decision, can be presented when assessing the cost-effectiveness of multiple options. To explore and explain potentially counterintuitive results that can arise when analyzing multiple options. METHODS: A template was created, based on the assumption of multivariate normality, in order to replicate a previous analysis that compared the cost-effectiveness of multiple options. We used this template to explain some of the different shapes that the cost-effectiveness acceptability curve (CEAC), cost-effectiveness acceptability frontier (CEAF), and expected value of perfection information (EVPI) may take, with changing correlation structure and variance between the multiple options. RESULTS: We show that it is possible for 1) an option that is subject to extended dominance to have the highest probability of being cost-effective for some values of the cost-effectiveness threshold; 2) the most cost-effective (optimal) option to never have the highest probability of being cost-effective; and 3) the EVPI to increase when the probability of making the wrong decision decreases. Changing the correlation structure between multiple options did not change the presentation of results on the cost-effectiveness plane. CONCLUSION: The cost-effectiveness plane has limited use in representing the uncertainty surrounding multiple options as it cannot represent correlation between the options. CEACs can represent decision uncertainty, but should not be used to determine the optimal decision. Instead, the CEAF shows the decision uncertainty surrounding the optimal choice and this can be augmented by the EVPI to show the potential gains to further research.

Cost-effectiveness acceptability curves and a reluctance to lose.

Cost-effectiveness acceptability curves (CEACs) are a method used to present uncertainty surrounding incremental cost-effectiveness ratios (ICERs). Construction of the curves relies on the assumption that the willingness to pay (WTP) for health gain is identical to the willingness to accept (WTA) health loss. The objective of this paper is to explore the impact that differences between WTP and WTA health changes have on CEACs. Previous empirical evidence has shown that the relationship between WTP and WTA is not 1:1. The discrepancy between WTP and WTA for health changes can be expressed as a ratio: the accept/reject ratio (which can vary between 1 and infinity). Depending on this ratio, the area within the southwest quadrant of the cost-effectiveness plane in which any bootstrap cost-effect pairs will be considered to be cost effective will be smaller, resulting in a lower CEAC. We used data from two clinical trials to illustrate that relaxing the 1:1 WTP/WTA assumption has an impact on the CEACs. Given the difficulty in assessing the accept/reject ratio for every evaluation, we suggest presenting a series of CEACs for a range of values for the accept/reject ratio, including 1 and infinite. Although it is not possible to explain this phenomenon within the extra-welfarist framework, it has been shown empirically that individuals give a higher valuation to the removal of effective therapies than to the introduction of new therapies that are more costly and effective. In cost-effectiveness analyses where uncertainty of the ICER covers the southwest quadrant of the cost-effectiveness plane, the discrepancy between societies' WTP and WTA should be indicated by drawing multiple CEACs.