Making Cost Effectiveness Analyses more useful: Budget Impact Curves Christopher McCabe PhD Endowed Research Chair in Emergency Medicine Research University of Alberta

Acknowledgements Funded by Genome Canada, Canadian Institutes for Health Research, Alberta Innovates Health Solutions, Capital Health Research Chair Endowment, UK National Institute for Health Research. Co-authors: Klemens Wallner

Overview The evolution of outputs from Cost Effectiveness Analysis (CEA) Synthesis and aggregation in CEA Meeting decision makers information needs Introducing Budget Impact Curves (BICs) An illustrative application of Budget Impact Curves in a Risk Sharing Scheme Budget Impact Curves: moving HTA towards procurement

The evolution of outputs from CEA Incremental Cost Effectiveness Ratios Incremental Cost Effectiveness Plane Confidence intervals in the cost effectiveness plane Cost Effectiveness Acceptability Curves Net Monetary and Net Health Benefit

The evolution of outputs from CEA Expected Value of Perfect Information Expected Value of Partial Perfect Information Expected Value of Sample Information Expected Net Benefit of Sampling Expected Net Present Value of Sample Information Net Benefit Probability Maps

Increasingly sophisticated characterisation of the aggregate effect of introducing a new technology. Maybe its time for something different.

Synthesis and aggregation Cost effectiveness models “…synthesize evidence on health consequences and costs from many different sources including data from clinical trials, observational studies, insurance claims databases, case registries, public health statistics and preference surveys….(in) a logical mathematical framework that permits the integration of facts and values …link these data to outcomes that are of interest to decision makers” Weinstein et al ViH 2003.

Synthesising evidence

“An ICER of £16,487 for concurrent treatment versus no trastuzumab.” Hall et al PharmacoEconomics 2011

Meeting Decision Makers Information Needs There are (at least) as many decision makers as there are budget holders. Most technologies impact on multiple budgets – CEA’s obsession with describing the aggregate impact implicitly assumes there is only one budget What if CEA gave budget specific information?

Introducing Budget Impact Curves Budget Impact Curves (CICs) report the expected impact of a new technology on specific budgets by capturing disaggregate cost information that is used in the calculation of the conventional aggregate CEA outputs. With time on the horizontal axis and cost on the vertical axis, BICs plot the expected incremental cost for specific budgets – such as the hospital budget or the pharmacy budget, over the time horizon of the model. Probability contours are used to plot the uncertainty in the budget impact estimates. BICs can be used to plot either the per-period or the cumulative budget impact. Budget holders can use the information provided to monitor actual budget impact against predictions, to help assess whether the promised value is actually being delivered.

Budget Impact Curve

An illustrative model ICER = $50,094 per QALY

Budget Impact Curve: Primary Care

Budget Impact Curve: Hospitals Tolerance range Contract review point

Price reduction ICER = $18,125 per QALY

Moving HTA towards procurement Reimbursement assumes successful implementation Procurement is key mechanism for effective implementation Standard HTA dossiers provide little if any useful information to support procurement Budget Impact Curves use information collected for conventional cost effectiveness analyses to help budget holders BICs might be a first girder in the bridge between system level reimbursement and provider level procurement

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Thank you