Methods in cost-effectiveness modelling

Projects:
Improved curve fits to summary survival data: Mean costs and quality-adjusted-life-years are central to the cost-effectiveness of health technologies. They are often calculated from time to event curves such as for overall survival and progression-free survival. Ideally, estimates should be obtained from fitting an appropriate parametric model to individual patient data. However, such data are usually not available to independent researchers. Instead, it is common to fit curves to summary Kaplan-Meier graphs, either by regression or by least squares. A more accurate method of fitting survival curves to summary survival data is investigated and an easy-to-use Excel spreadsheet to implement the method is available here https://www.mq.edu.au/research/research-centres-groups-and-facilities/prosperous-economies/centres/centre-for-the-health-economy/our-people/team-bios/prof-hoyle.

Multiple patient cohorts: Most health technology economic evaluations simulate only the prevalent cohort or the next incident cohort of patients.  We investigate how to estimate and aggregate the incremental cost effectiveness ratios (ICERs) for both currently eligible (prevalent) and future (incident) patient cohorts within the same model-based analysis.  Assuming multiple incident cohorts and decreasing real price of drugs, all drugs are predicted to be better value than assuming just a single incident cohort.

Assumptions for future drug prices in HTA: Cost-effectiveness analyses worldwide assume that the price of any single drug increases with inflation.  The objective of this analysis is to challenge the widespread assumption that the price of any single drug increases with inflation in the UK, and to calculate the impact on the incremental cost-effectiveness ratio (ICER) of using a more realistic estimate for the future price of individual drugs.  In this analysis the mean annual decrease in the real price of individual drugs was calculated as 3.8%.

Publications:
Hoyle, M., Henley, W. Improved curve fits to summary survival data: application to economic evaluation of health technologies. BMC Medical Research Methodology. 2011; 11:139. Open Access

Hoyle, M., Anderson, R. Whose drugs and benefits? Why economic evaluations should simulate both prevalent and all future incident patient cohorts. Medical Decision Making. 2010; 30(4):426-437. Abstract

Hoyle, M. Accounting for the drug life cycle and future drug prices in cost-effectiveness analysis. Pharmacoeconomics. 2011; 29(1):1-15. Abstract

Hoyle, M. Future drug prices and cost-effectiveness analyses. Pharmacoeconomics. 2008; 26(7), 589-602. Abstract

People involved: Martin Hoyle, Rob Anderson