2F-5 THE COST-EFFECTIVENESS MODEL OUTPUT TOOL

Monday, June 13, 2016: 15:15
Stephenson Room, 5th Floor (30 Euston Square)

Mike Paulden, MA., MSc., University of Alberta, Edmonton, AB, Canada, Mohsen Sadatsafavi, University of British Columbia, Vancouver, BC, Canada, Nick Bansback, PhD, University of British Columbia; Centre for Clinical Epidemiology and Evaluation; Centre for Health Evaluation and Outcome Sciences, Vancouver, BC, Canada and Christopher McCabe, PhD, Department of Emergency Medicine, University of Alberta, Edmonton, AB, Canada
Purpose: Decision makers are increasingly making use of advanced outputs from cost-effectiveness analyses (CEAs), including estimates of the uncertainty around model results and the expected value of further information for parameters. Estimating and presenting these outputs can be time-consuming for analysts, and inconsistencies in the range of outputs presented by different analysts can be problematic for decision makers. With support from CADTH and Genome Canada, we have developed a tool that alleviates these issues by automatically producing a wide range of standardized outputs from CEAs.

Method(s): The Cost-Effectiveness Model Output (CEMO) tool was developed in Microsoft Excel. Analysts must enter information about their model, including the strategies, cost perspectives considered (e.g., health sector, societal), effectiveness outcomes considered (e.g., QALYs, life years), number of Markov cycles, time horizon, parameters varied in sensitivity analyses, and the number of Monte Carlo simulations used in probabilistic analysis. The tool uses this information to create a customized ‘Model Results’ worksheet that is unique to the analyst’s model. Analysts can use their preferred software to develop their model, before exporting the raw results into the Model Results worksheet. The tool then uses these raw results to calculate present values (if necessary – the tool supports differential and/or non-constant discounting if required), estimates of net health benefit (NHB) and net monetary benefit (NMB), the expected value of perfect information (EVPI), and estimates of the expected value of partial perfect information (EVPPI) for each parameter. The tool also automatically generates a standardized set of tables and figures for the analyst to report to decision makers.

Result(s): The CEMO tool provides the following outputs: (1) for deterministic and probabilistic analyses, separate tables of costs and effects, incremental costs and effects, incremental cost-effectiveness ratios (ICERs), NHB, NMB, the ranking of strategies by cost-effectiveness, and the probability that each strategy is cost-effective (probabilistic analysis only); (2) plots on the cost-effectiveness plane for all analyses; (3) results tables and ‘tornado’ graphs for one-way sensitivity analyses; (4) results tables for two-way sensitivity analyses; (5) cost-effectiveness acceptability curves (CEACs) and the cost-effectiveness acceptability frontier (CEAF) for probabilistic analyses; (6) tables and figures reporting EVPI and EVPPI.

Conclusion(s): The CEMO tool reduces the burden on analysts who conduct CEAs and improves the consistency of the data considered by decision makers.

See more of: ORAL ABSTRACTS: APPLIED HEALTH ECONOMICS

See more of: 16th Biennial European Conference