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Where next for economic and comparative effectiveness evidence in an era of personalized/ precision medicine. Stuart Peacock Canadian Centre for Applied Research in Cancer Control University of British Columbia British Columbia Cancer Agency. Overview.
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Where next for economic and comparative effectiveness evidence in an era of personalized/ precision medicine Stuart Peacock Canadian Centre for Applied Research in Cancer ControlUniversity of British ColumbiaBritish Columbia Cancer Agency
Overview • A little bit about the Canadian Centre for Applied Research in Cancer Control (ARCC) • Why is economic evidence important? • Economic evidence – a personalized medicine case study in colorectal cancer • Some challenges ahead relating to personalized medicine amongst other things • The importance of preferences as part of evidence
ARCC - the big question? How can we inform and promote cancer control policies and practices (fromprevention to palliative care and survivorship) that are evidence-based, sustainable and ethical in order to reduce the burden of cancer for Canadians?
How do you answer the big question? • In the mid-2000s, the Canadian Cancer Society committed to invest in a national centre dedicated to health economics, services, policy and ethics in cancer control • Identified a need for an organization that both • brings together these multiple disciplines to address cancer control questions and • provides national leadership in research, knowledge translation and capacity building in this area
Centre Approach • Interdisciplinary, inter-provincial and inter-professional with a commitment to cross-national collaboration • ARCC is an unique partnership of 30 investigators, 30 associates and over 40 research staff from across Canada • Two leadership hubs at the British Columbia Cancer Agency (BCCA)/University of British Columbia (UBC) and at Cancer Care Ontario (CCO)/University of Toronto (U of T)
Research Health Technology Assessment Patients and Families Societal Values and Public Engagement ARCC Knowledge Translation Health Systems, Services and Policy • Five thematic program areas:
Why is economic evidence important? “Allocation of funds and facilities are nearly always based on the opinion of consultants but, more and more, requests for additional facilities will have to be based on detailed arguments with ‘hard evidence’ as to the gain to be expected from the patient’s angle and the cost. Few could possibly object to this.” Cochrane AL. Effectiveness and Efficiency: random reflections on health services. Nuffield Provincial Hospitals Trust, London, 1972.
Oncology drugs in British Columbia 15% 16% 14% 14% 11% 22% 19% 9% 19% 28%
Monthly and median costs of FDA approved cancer drugs (2007 US$) Alemtuzumab Nelarabine Aldesleukin Denileukin Cetuximab Source: Bach, NEJM 2009
Why is economic evidence important? Safety Efficacy Quality Cost-Effectiveness The Fourth Hurdle
Economic evaluation for reimbursement decisions • Many jurisdictions now require economic evaluation for reimbursement decisions (primarily for drugs) • Accompanied by guidelines for pharmaceutical companies • Pricing decisions maybe linked with reimbursement decisions • Australia: Pharmaceutical Benefits Advisory Committee (PBAC) • England and Wales: National Institute for Health and Clinical Excellence (NICE) • Based on ‘Acceptable’ Incremental Cost-Effectiveness Ratios (ICERs)
Health economic evaluation Impact on health status • Survival • Quality of life New Program • Hospitalisations • Drugs, procedures etc. Impact on health care costs Target patient group Impact on health status • Survival • Quality of life • Hospitalisations • Drugs, procedures etc. Old Program Impact on health care costs
Incremental Cost-Effectiveness Ratio (ICER) (Costnew – Costold) (Effectivenessnew – Effectivenessold) = ICER ICER =C / E Incremental health effects gained by using the intervention Incremental resources required by the intervention
ICER thresholds and the Australian PBAC ICER thresholds in Australia Source: George et al. Pharmacoeconomics 2001
Cancer ICER thresholds in England and Wales Source: Rawlings. Lancet Oncology 2007
A personalized medicine case study: Cetuximab (Erbitux) in advanced colorectal cancer (CRC)
Cetuximab in Advanced Colorectal Cancer • Treatment for advanced colorectal cancer (CRC) – improves overall and progression-free survival compared to best supportive care1 • Mechanism of action - monoclonal antibody that targets epidermal growth factor receptor (EGFR), modulating tumor cell growth2 • Resistance to cetuximab is common (>50% after one treatment) - Caused by mutations in component of EGFR: K-ras protein, which occur in ~40% of patients3
Cetuximab in Advanced Colorectal Cancer Effectiveness of cetuximab (overall and progression-free survival) is significantly associated with k-ras mutation status (p>0.001) - Patients with wild-type k-ras tumors did benefit (overall survival 9.5 months) - Patients with mutated k-ras tumors did not benefit (overall survival 4.8 months)3 Source: Karapetis et al, NEJM, 2009
Cetuximab in Advanced Colorectal Cancer Cost-effectiveness • Cetuximab may increase the already significant cost of managing advanced CRC, especially when provided to all patients • Drug and administration cost of cetuximab $71,000/patient4 • K-ras testing $450/patient4
Cetuximab in Advanced Colorectal Cancer Cost-effectiveness cont… • Providing drug to all patients is not cost effective • Incremental cost effectiveness ratio (ICER) ~$300,000 per QALY gained5 • Targeting the therapy to patients with wild-type k-ras improves cost-effectiveness • ICER ~$180,000/QALY5 • Theoretical cost-savings associated with treating only wild-type k-ras, $740 million (US), accounting for cost of k-ras testing
Identifying the costs of testing strategies Current reimbursement systems for diagnostic tests are cost based rather than value based Tests for multiple markers cost thousands of dollars Technology is changing – cost per single-nucleotide polymorphism (SNP) analyzed is falling rapidly True opportunity cost is often unknown – testing may result in changes to medical utilization Difficult to estimate a true economic value at any given time
Sensitivity, specificity and complexity Genetic tests share the same concerns about sensitivity and specificity as older diagnostics But, we have many years of experience modeling screening interventions Patient outcomes are likely to be influenced by multiple genes, and each gene can influence multiple outcomes Each outcome is modified by interactions with other genes and environmental exposures – including diets, drugs and disease states
Lack of effectiveness data Lack of data on patient and clinician behaviour following the results of diagnostic tests, and associated patient outcomes Issue gets more complicated if the test indicates a patient should not get a drug Are there alternatives? If so, how does the analysis factor in the timing and sequencing of alternatives?
Lack of effectiveness data cont … Inconclusive or contradictory results from small (n < 200) RCTs may be insufficient for robust estimates of effectiveness More decision analytic modelling required, with careful consideration of parameter and decision uncertainty Use of surrogate end-points, e.g. progression free survival, is likely to increase Cumulative synthesis of RCTs needed
Coverage with evidence development (CED) Uncertainty with the clinical value of new technologies will likely mean that the value of additional research and policy options, such as CED, should be considered CED = provisional approval for coverage by payers on condition that additional data on effectiveness are collected through RCTs or patient registries Registries and linkable administrative data sets will only become more important
Personalized vs. stratified medicine Is personalized medicine more likely to lead to smaller and smaller sub-group analyses rather than ‘individualized’ care? Possibly, because the marginal cost of developing new drugs will outweigh the marginal benefits for pharmaceutical companies RCTs and economic evaluation will not become redundant – but they will be more complex and costly Linkable administrative data will be very important
Preferences and personalized medicine Personalized medicine promises to provide tailored therapies that take into account individual differences in risk and values The balance of risks and benefits for each person will differ because of preference heterogeneity Tailoring therapy and determining the optimal strategy will mean listening to patients preferences Economic evaluations need to model patient preferences about different treatment options
Preferences count in many circumstances PSA screening appears to prolong life expectancy but shortens quality-adjusted life expectancy i.e. it is a preference sensitive decision Men who receive decision support are more knowledgeable, have less decisional conflict, and are less inclined to undergo PSA screening Sexual and urinary dysfunction after treatment have modest effects on global quality of life Men with low literacy do not understand many prostate cancer terms Preferences should be part of guideline development (Krahn and Naglie, JAMA 2008)
A cautionary tale ... ... on inflating community expectations
Inflating community expectations At the February 1, 2012 data cut-off, median follow-up was 12.5 months for vemurafenib and 9.5 months for dacarbazine. In patients not censored at crossover, median OS was 13.6 months for vemurafenib vs. 10.3 months for dacarbazine (HR 0.76; P<0.01 post-hoc). In those censored at crossover, OS was 13.6 months for vemurafenib and 9.7 months for dacarbazine (HR 0.76; P<0.001 post-hoc). (BRIM3 Trial presentation at ASCO 2012)
Acknowledgements ARCC is funded by the Canadian Cancer Society Email: speacock@bccrc.ca ARCC website: www.cc-arcc.ca
