May 2017 - Health economic evaluations
On Wednesday May 24th, Professor Chris Bojke (Faculty of Medicine and Health, University of Leeds) visited Groningen to talk about his latest research: "New insights into health economic evaluations of medical technologies". The seminar was organised by Centre of Expertise Healthwise and the signature area Individual Health & The Economic Environment.
About Chris Bojke
Chris Bojke is an empirical health economist, specialised in the analysis of large and complex datasets for the purpose of making policy decisions on cost-effectiveness models.
Recent research includes:
- Quality-adjusted measurement of NHS productivity over time.
- Estimation of top-up payments for reimbursement of highly specialised treatment.
- The impact of NHS medical revalidation on the quality and quantity of consultant output and variation in patient reported outcomes across hospitals and their relationship to cost.
Chris is Chair in Health Economics at the Faculty of Medicine and Health, University of Leeds. He has a PhD in Economics from the University of Groningen and an MSc in Statistics from the University of Newcastle. Outside health economics, Chris maintains an interest in sports and education economics.
Explicit Population Averaging in Cohort Models of Economic Evaluation of Health Technologies
Health-economic evaluation of new medical technologies typically use non-linear statistical techniques, such as parametric survival analysis, to populate cohort models. The objective of such an evaluation is to help decision makers identify whether provision of the new technology to a population of eligible patients is likely to produce an overall net increase in the total Health-Related Quality of Life. Accommodating heterogeneity within the patient population has largely been restricted to the a priori identification of specific subgroups of patients for whom the economic arguments might be expected to systematically differ.
However, fundamental features of non-linear models are: that the omission of relevant heterogeneity in the regression models can lead to biased results in population models and that, even with unbiased results, the expectation of an average population member is not the average of the expectations across a heterogeneous population. In both cases, without explicitly incorporating the heterogeneity across the population, model output may ultimately present a misleading picture to decision makers.
Chris' presentation will highlight the potential dangers of ignoring heterogeneity and identify potential solutions to the issue of accommodating observed and unobserved heterogeneity in cohort models without having to resort to computational expensive individual patient simulation.
|Last modified:||30 May 2017 10.04 a.m.|