Does Cost-Effectiveness Analysis Discriminate against Patients with Short Life Expectancy?

Does the use of quality-adjusted life-years (QALYs) in cost-effectiveness analyses (CEAs) of health care interventions necessarily discriminate against patients with short life expectancy compared with others? This paper reviews the arguments both that it does and that it does not, and demonstrates that whether the use of any time-dependent outcome measure in CEA will result in discrimination depends, in the context of any given choice between interventions, upon the choice of cost-effectiveness ‘threshold’ adopted by the decision maker, whether the incremental cost-effectiveness ratio (ICER) of the intervention for a subgroup of patients with relatively short life expectancy lies above the cost-effectiveness threshold, and whether the ICER for a subgroup of patients with longer life expectancy falls below the cost-effectiveness threshold. For discrimination to result against such patients requires that the long term ratio of costs to QALYs associated with the intervention be lower than the short term ratio of costs to QALYs. The implications for agencies which use CEA as part of their decision making are then discussed.

Budget allocation and the revealed social rate of time preference for health

Appropriate decisions based on cost-effectiveness evaluations of health care technologies depend upon the cost-effectiveness threshold and its rate of growth as well as some social rate of time preference for health. The concept of the cost-effectiveness threshold, social rate of time preference for consumption and social opportunity cost of capital are briefly explored before the question of how a social rate of time preference for health might be established is addressed. A more traditional approach to this problem is outlined before a social decision making approach is developed which demonstrates that social time preference for health is revealed through the budget allocations made by a socially legitimate higher authority. The relationship between the social time preference rate for health, the growth rate of the cost-effectiveness threshold and the rate at which the higher authority can borrow or invest is then examined. We establish that the social time preference rate for health is implied by the budget allocation and the health production functions in each period. As such, the social time preference rate for health depends not on the social time preference rate for consumption or growth in the consumption value of health but on growth in the cost-effectiveness threshold and the rate at which the higher authority can save or borrow between periods. The implications for discounting and the policies of bodies such as NICE are then discussed.Economic evaluation. Discounting. Cost-effectiveness analysis

Does cost-effectiveness analysis discriminate against patients with short life expectancy? Matters of logic and matters of context

The aim of this paper is to explore the claim of ageism made against the National Institute for Health & Clinical Excellence and like organisations, and to identify circumstances under which ageist discrimination might arise. We adopt a broad definition of ageism as representing any discrimination against individuals or groups of individuals solely on the basis that they have shorter life expectancy than others. A simple model of NICE?s decision making process is developed which demonstrates that NICE?s recommendations do not inherently discriminate on the basis of life expectancy per se but that scope for discrimination may arise in the case of specific technologies having identifiable characteristics. Such discrimination may favour patients with either longer or shorter life expectancy. It is shown that NICE?s policies, procedures and the context in which NICE makes its decisions not only reduce the scope for discriminatory recommendations but also – in the case of “end of life” treatments – increase the likelihood that NICE?s recommendations favour those with shorter, rather than longer, life expectancy.

The cost-effectiveness of EndoPredict to inform adjuvant chemotherapy decisions in early breast cancer

Background Adjuvant chemotherapy in breast cancer patients post resection has been estimated to reduce mortality rates by up to 30%. However, the heterogeneous nature of the disease and patients implies that not all patients should receive the treatment. Many existing prognostic tools, may not definitively estimate the most effective treatment strategy, resulting in an indeterminate risk classification. In such cases gene expression profiling tests can aid the treatment decision. Methods This study evaluated the cost-effectiveness of EndoPredict in patients with indeterminate risk classification. A mathematical model was constructed to determine how the change in treatment decisions impacted the long term health of the population, and the future cost implications to the NHS. Results EndoPredict was found to lead to 36.9% of patients having a change in treatment decision. As a result its use was found to result in an increase in population health but also in total costs, resulting in an incremental cost-effectiveness ratio of £26,836 per quality adjusted life year. This was subject to significant parametric and structural uncertainty. Conclusion While EndoPredict was found to be more expensive overall, its ability to affect a more optimal allocation of chemotherapy, resulted in long term health gains, however, they were insufficient to justify the high cost of EndoPredict

Cost effectiveness of personalized treatment in women with early breast cancer: the application of OncotypeDX and Adjuvant! Online to guide adjuvant chemotherapy in Austria

A Breast Cancer Outcomes model was developed at the ONCOTYROL research center to evaluate personalized test-treatment strategies in Austria. The goal was to evaluate the cost-effectiveness of a new 21-gene assay (ODX) when used in conjunction with the Adjuvant! Online (AO) decision aid to support personalized decisions about use of adjuvant chemotherapy in early-stage breast cancer patients in Austria. We applied a validated discrete-event-simulation model to a hypothetical cohort of 50 years old women over a lifetime horizon. The test-treatment strategies of interest were defined using three-letter acronyms. The first (second, third) letter indicates whether patients with a low (intermediate, high) risk according to AO were tested using ODX (Y yes, N no). The main outcomes were life-years gained, quality-adjusted life-years (QALYs), costs and cost effectiveness. Robustness of the results was tested in sensitivity analyses. Results were compared to a Canadian analysis conducted by the Toronto Health Economics and Technology Assessment Collaborative (THETA). Five of eight strategies were dominated (i.e., more costly and less effective: NNY, NYN, YNN, YNY, YYN). The base-case analysis shows that YYY (ODX provided to all patients) is the most effective strategy and is cost effective with an incremental cost-effectiveness ratio of 15,700 EUR per QALY gained. These results are sensitive to changes in the probabilities of distant recurrence, age and costs of chemotherapy. The results of the base-case analysis were comparable to the THETA results. Based on our analyses, using ODX in addition to AO is effective and cost effective in all women in Austria. The development of future genetic tests may require alternative or additional test-treatment strategies to be evaluated. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40064-015-1440-6) contains supplementary material, which is available to authorized users

A framework for statistical modelling of the extremes of longitudinal data, applied to elite swimming

We develop methods, based on extreme value theory, for analysing observations in the tails of longitudinal data, i.e., a data set consisting of a large number of short time series, which are typically irregularly and non-simultaneously sampled, yet have some commonality in the structure of each series and exhibit independence between time series. Extreme value theory has not been considered previously for the unique features of longitudinal data. Across time series the data are assumed to follow a common generalised Pareto distribution, above a high threshold. To account for temporal dependence of such data we require a model to describe (i) the variation between the different time series properties, (ii) the changes in distribution over time, and (iii) the temporal dependence within each series. Our methodology has the flexibility to capture both asymptotic dependence and asymptotic independence, with this characteristic determined by the data. Bayesian inference is used given the need for inference of parameters that are unique to each time series. Our novel methodology is illustrated through the analysis of data from elite swimmers in the men's 100m breaststroke. Unlike previous analyses of personal-best data in this event, we are able to make inference about the careers of individual swimmers - such as the probability an individual will break the world record or swim the fastest time next year.Comment: 28 pages, 4 figure

Budget Allocation and the Revealed Social Rate of Time Preference for Health

Appropriate decisions based on cost-effectiveness evaluations of health care technologies depend upon the cost-effectiveness threshold and its rate of growth as well as some social rate of time preference for health. The concept of the cost-effectiveness threshold, social rate of time preference for consumption and social opportunity cost of capital are briefly explored before the question of how a social rate of time preference for health might be established is addressed. A more traditional approach to this problem is outlined before a social decision making approach is developed which demonstrates that social time preference for health is revealed through the budget allocations made by a socially legitimate higher authority. The relationship between the social time preference rate for health, the growth rate of the cost-effectiveness threshold and the rate at which the higher authority can borrow or invest is then examined. We establish that the social time preference rate for health is implied by the budget allocation and the health production functions in each period. As such, the social time preference rate for health depends not on the social time preference rate for consumption or growth in the consumption value of health but on growth in the cost-effectiveness threshold and the rate at which the higher authority can save or borrow between periods. The implications for discounting and the policies of bodies such as NICE are then discussed