economic evaluation, reimbursement and context. how to assess personalised medicines for...
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How to assess personalised medicines for reimbursement decisions?TRANSCRIPT
Adelaide Health Technology Assessment (AHTA)
Life Impact | The University of Adelaide
Monday, June 25, 2012
HOW TO ASSESS PERSONALISED MEDICINES FOR REIMBURSEMENT DECISIONS?
Developing a framework for Australia
Tracy Merlin, Claude Farah, Camille Schubert, Andrew Mitchell, Janet E Hiller, Philip Ryan
Assessing personalised medicine
• To adopt pharmacogenetics/genomics into clinical practice, a PGx test should demonstrate analytical validity, clinical validity, and clinical utility (DHHS, 2008).
– Analytical validity – how accurately and consistently a test detects the presence of a specific genotype.
– Clinical validity – the accuracy with which a test detects or predicts a given phenotype (clinical disorder or outcome).
– Clinical utility – the net balance of risks and benefits for use of the test in clinical practice eg impact on patient health outcomes of targeting drug treatment according to presence of biomarker
→ Needed for reimbursement decision
Problem
• Systematic review of pharmacogenetic studies (Holmes et al 2009)» 1987-2007, Medline, k=1668 studies» Review:study ratio (25:1) - high expectation but limited
translation of research» Small sample sizes» Primarily a candidate gene approach using common alleles
rather than genome-wide analysis» Surrogate rather than clinical outcomes» Likely ‘significance chasing’ ie post hoc subgroup analysis
or publication bias
No system in place nationally for evaluating co-dependent technologies, specifically personalised medicines, for reimbursement decisions
National initiatives
No formal systems in place internationally for evaluating personalised medicines for reimbursement decisions
Project outline. I.
Aim
• To develop an approach for evaluating a biomarker/test/ drug (‘co-dependent technologies’) package to inform a national reimbursement decision.
Methods – Stage 1
• Personalised medicine case studies identified from recent reimbursement applications in Australia – information commonalities / gap analysis
• Relevant international regulatory and reimbursement guidance documents reviewed
Slide 7© T. Merlin 2011
Case study(biomarker/ therapy)
Decision-making body
(therapeutic purpose)
Evidence quality Evidence gaps(N=67
information items)
Test reimbursed?
Drug reimbursed?
EGFR/gefitinib for non small cell lung cancer (2nd line)
PBAC(targeted
treatment)
• No direct evidence
• Linked evidence » moderate quality
8/67 (12%) Not considered Yes
K-RAS/cetuximab for metastatic colorectal cancer (1st line)
PBAC(targeted
treatment)
• No direct evidence
• Linked evidence » poor quality
32/67 (48%) Not considered No
K-RAS/ panitumumab for metastatic colorectal cancer (2nd line)
PBAC(targeted
treatment)
• No direct evidence
• Linked evidence » poor quality
21/67 (31%) Not considered No
PDGFR rearrangements/ imatinib for primary or secondary clonal eosinophilia
MSAC(targeted
treatment)
• Direct evidence » poor quality
• Linked evidence » moderate quality
3/67 (4%) Yes Yes
KIT D816V/ imatinib for aggressive systemic mast cell disease without eosinophilia (2nd line)
MSAC(rule out imatinib
treatment)
• Direct evidence » poor quality
• Linked evidence » moderate quality
4/67 (6%) No Yes
Project outline. II. Methods – Stages 2 & 3
• Collated information synthesised into a cohesive structure amalgamating PBAC and MSAC evidentiary needs
• Clinical effectiveness information grounded in Bradford-Hill causality theory → explain association between biomarker and drug treatment outcomes– strength, specificity and temporality of association– consistency and coherence of effect– biological plausability and gradient (eg dose response)– producing effect upon experimentation or by analogy.
• Trialled on case studies
• Circulated to federal policy-makers and technical experts for input + national public consultation.
Results - decision framework
1. the drug is cost-effective in untested population, but cost-ineffective when conditioned on the biomarker identified by the test – drug funded
– test not funded. Biomarker is not explanatory. Or test is not accurate.
2. the drug is cost-effective in untested population but even more cost-effective when conditioned on the biomarker identified by the test– drug funded
– test may be funded......uncertainty surrounding association between biomarker and drug treatment effect will drive the decision to reimburse the test
Decision framework
3. the drug is not cost-effective in untested population but is cost-effective when conditioned on biomarker identified by the test
– public funding of both test and drug depends on uncertainty surrounding the association between biomarker and drug treatment effect
4. the drug is not cost-effective in untested and tested population– neither drug or test should be reimbursed.
Methodological framework
Section B [Q20]– Clinical benefit
• Test safety / effectiveness• Biomarker-drug relationship (treatment effect
modification, prognostic impact)
Option 1: Direct evidence (hierarchy) [Q21-24]and/or
Option 2: Linked evidence support [Q25-39]
Section C [Q40-42]– Translate biomarker/test/drug evidence to
Australian clinical setting (applicability, extrapolation, transformation)
Section D [Q43-71]– Economic model. (includes test/no test arms)
• Section E [Q72-79]– Financial impact
Adapted PBAC Application structure:
Section A [Q1-19]– Context for submission– Rationale for submission– Proposed impact on current clinical practice
Two key methodological areas that impact on funding decision
1. Likelihood that results are correct?Relates to policy-maker’s certainty regarding the validity of the results
– effect of bias and confounding
2. What is the association between biomarker and drug?• Treatment effect modification
• Prognostic impact
• Both
• Unknown
How to elucidate this relationship from the evidence base?
Section B - the evidence base
Outcome Biomarker +ve Biomarker -ve
Drug AN=100
Drug BN=100
Drug AN=100
Drug BN=100
5 year OS 60% 30% 30% 30%
RR=2.0 [1.4, 2.8] RR=1.0 [0.7, 1.5]
5 year OS 70% 60% 35% 30%
RR=1.2 [1.0, 1.4] RR=1.2 [0.8, 1.7]
5 year OS 60% 40% 20% 20%
RR=1.5 [1.1, 2.0] RR= 1.0 [ 0.6, 1.7]
Simplistic example
2x
1. Treatment effect modification
2. Prognostic impact
3. TEM + Prognostic impact
Randomise to test
Test
+ve
Drug A Drug B
-ve
Drug A Drug B
No test
Drug A Drug B
Randomise to drug
Randomise to drug
Hypothetical RCT as conceptual frameworkIs there a difference in
Drug A vs B effectiveness in unselected population?
Linkage 2
Biomarker-stratified design:Is there a difference in Drug A vs B effectiveness when conditioned on biomarker? Linkage 1
Health Outcomes
+e +ve
Randomise to test
Test
+ve
Drug A Drug B
-ve
Drug A Drug B
No test
Drug A Drug B
Randomise to drug
Randomise to drug
Linking evidence
Health Outcomes
Enrichment design:Is there a difference in Drug A vs Beffectiveness when conditioned on biomarker +ve? Linkage 2
-ve+ve -ve+ve
Post-treatment test or test archival tissue
Linkage 1
Test accuracy -Linkage 3
Project outline. II.
Results
• Developed a decision framework for policy makers
• Developed a methodological framework to support the decision making
– 79 item checklist must be addressed when personalised medicine submitted for funding through Medicare (test) and the Pharmaceutical Benefits Scheme (drug). http://www.health.gov.au/internet/hta/publishing.nsf/Content/co-1
– Forms basis of co-dependent technology submission guidelines (not yet released)
Conclusion
Merlin T, Farah C, Schubert C, Mitchell A, Hiller JE, Ryan P. Assessing personalised medicines in Australia: A national framework for reviewing co-dependent technologies. Journal of Medical Decision Making, 2012. [In press].
• Co-authors - Claude Farah, Camille Schubert, Andrew Mitchell, Janet Hiller, Philip Ryan
• Feedback on framework- Sarah Lord, Robyn Ward, Graeme Suthers, Brian Richards, Lloyd Sansom
• Part-funding – Australian Government Department of Health & Ageing