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Subgroup analyses in cost- effectiveness to support health technology assessments
Chrissie Fletcher, Amgen Ltd PSI Journal Club Nov 2014
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Acknowledgements
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Subgroup analyses in cost- effectiveness to support health - - PowerPoint PPT Presentation
Subgroup analyses in cost- effectiveness to support health - - PowerPoint PPT Presentation
Subgroup analyses in cost- effectiveness to support health technology assessments Chrissie Fletcher, Amgen Ltd PSI Journal Club Nov 2014 1 Acknowledgements 2 Disclaimer The views expressed herein represent those of the presenter and do
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Disclaimer
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- The views expressed herein represent
those of the presenter and do not necessarily represent the views or practices of Amgen.
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Overview
- Introduction
- Statistical considerations in cost-
effectiveness analyses
- Guiding principles for subgroups in cost-
effectiveness
- Example case study
- Discussion and conclusions
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Introduction
- “Health technology assessment (HTA) is a
form of policy research that examines the short-term and long-term social consequences (e.g. societal, clinical, economic, ethical and legal) of the application of technology” (Banta D)
- Cost-effectiveness analysis (CEA) seeks
to identify technologies that if funded, maximise total population health
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Introduction (cont)
- Subgroup analyses in CEA help to
understand key drivers of CE, quantify the uncertainty and extent of heterogeneity
- Many national pharmacoeconomic
guidance's provide recommendations on how to assess patient heterogeneity but there is no consensus on which specific methods are most appropriate
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Introduction (cont)
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Principles and best practices for subgroup analyses (Paget et al)
- Subgroups pre-specification & definition
- Subgroup by treatment interaction
- Multiplicity issues
- Sensitivity analyses
- Replication
- Source of evidence
- Presenting and reporting subgroup results
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Statistical considerations in CEA
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Overall CEA CEA in subgroups
Random variation? Generalisability ? Cost to identify subgroup ? Subgroup definition ? Number of subgroups ? Uncertainty ?
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When is a subgroup cost- effective?
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Subgroup effects can be predictive or prognostic
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Subgroup Response
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Standard of care New treatment
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Predictive subgroup
Subgroup Response
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Standard of care New treatment
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Prognostic subgroup
Subgroup Response
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Standard of care New treatment
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No subgroup effect
Concluding a subgroup is predictive can depend
- n the scale used to
measure the treatment effect
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Subgroups for regulatory vs reimbursement
Regulatory
- Assess consistency of
treatment effects in individual subgroup factors
- Balance of benefit and
risks Reimbursement
- Assess total budget
impact of multiple subgroup factors considered jointly (optimise healthcare spend under constraints)
- Balance benefit, risks and
costs
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Both concerned with ‘optimism’ bias, biological plausibility, credibility, and relevance to clinical practice
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Planning subgroups in confirmatory trials
- Consider for both regulators and payers
- Regulatory (EMA draft guideline)
– Assessing consistency, biological plausibility, replication, pre-specification and multiplicity
- Payers (EUnetHTA, NICE methods)
– Absolute effects (scale), biological and economic plausibility, pre-specification, quantify heterogeneity (estimation)
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Useful approaches in subgroup analyses for CEA
- Bayesian analysis to shrink subgroup
results towards average population
- Probabilistic sensitivity analyses (PSA)
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Costs QALY
Rejection threshold Acceptance threshold
Zone of uncertainty
Dominated Dominant
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Guiding principles for cost- effectiveness subgroup analyses
Clinical effectiveness
- Sensitivity analyses
- Presenting and reporting
- Replication
- Source of evidence
- Pre-specification of
subgroups
- Multiplicity issues
- Subgroup by trt
interaction Cost-effectiveness
Desirable Less important Extremely important Important All data sources Transparency
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Example case study
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Discussions and conclusions
- Subgroup analyses are important in CEA
- Many subgroup analyses guiding principles
for clinical effectiveness apply to CEA
- Importance of documenting methods,
assumptions and sensitivity analyses
- Statisticians and health economics together
should plan analytical strategies for CEA early in the drug development process
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