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R. Brett McQueen, PhD
Health Economist and Head of Statistics
Research in Real Life (RiRL)/Observational & Pragmatic Research Institute (OPRI)
Adjunct Assistant Professor
University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences
Using the right ‘E’
(E-fficacy vs. E-ffectiveness) in cost-
effectiveness/affordability assessments
ISPOR Real-World Task Force
• In 2007, the International Society for Pharmacoeconomics and Outcomes Research
(ISPOR) Real-World Data Task Force published a statement in support of using real-
world data for coverage and reimbursement decisions
• Defined as economic, clinical, or patient-centered data NOT derived from efficacy
randomized controlled trials (RCTs)
• Pragmatic trials, registries, administrative data, health surveys, electronic records, etc.
• Task force called for efficacy and effectiveness to be used to achieve the “fourth
hurdle,” demonstration of cost-effectiveness to gain coverage and reimbursement*
2
*Garrison LP Jr, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task
Force report. Value Health 2007;10:326–335.
Does the drug work and provide good value for money?
Randomized Controlled Trials (RCTs)
Coverage and Reimbursement bodies:
(i.e. Public and Private Payers)
Hurdle 4
Cost-Effectiveness
Regulatory Bodies: (i.e. US Food and Drug Administration,
European Medicines Agency)
Hurdles 1–3
Safety, Efficacy, Production
RCT-Informed
Best Available
Evidence
Trial-Based CEA Model-Based CEA
Can the drug be used safely?
Can the drug work?
Can it be produced to high quality?
End User: Clinicians and Patients
Campbell JD, McQueen RB, Briggs A. The “E” in Cost-Effectiveness Analyses: A Case Study of Omalizumab Efficacy and Effectiveness for Cost-Effectiveness Analysis Evidence. Ann Am Thorac Soc. 2014 Feb; 11 Suppl 2: S105-11.
Efficacy versus effectiveness data for cost-effectiveness studies
• Cost-effectiveness using efficacy
• Potential value of treatment tested in ideal environment (e.g., artificially enhanced
adherence, protocol-driven resource utilization)
• “Piggyback” study or model forecasting trial results
• Cost-effectiveness using effectiveness data
• Potential value relevant to decision-makers and patients*
• Modeling with real-world evidence (e.g., adherence-adjusted treatment effects,
real-world resource utilization)
• Each can address different yet equally important questions
• “Bounds” of evidence for adoption or rejection of a technology§
*Callahan KP, Bridges JF. Using comparative effectiveness research to inform-decision making: is there a role of economic evaluation? Journal of Comparative Effectiveness Research; 1(4), 299-301 (2012).
§ Campbell JD, McQueen RB, Briggs A. The “E” in Cost-Effectiveness Analyses: A Case Study of Omalizumab Efficacy and Effectiveness for Cost-Effectiveness Analysis Evidence. Ann Am Thorac Soc. 2014 Feb; 11 Suppl
2: S105-11.
CEA Steps
1.Define the cost-effectiveness objective, state the perspective of the analysis that will
determine what costs are relevant, select the alternative intervention(s), select the
study design
2.Define and measure the components of cost (Ci) and effect (Ei) by alternative
intervention,
3.Compute the incremental cost-effectiveness ratio,
4.Conduct sensitivity and/or uncertainty analyses,
5.Communicate findings and their limitations.
Omalizumab CEA Case Study: Cost-Efficacy versus Cost-
Effectiveness: Is There a Difference?
• Compare and contrast two European omalizumab cost-effectiveness
decision-analytic models:
• One drawing on data from explanatory RCT evidence (Dewilde and colleagues)
• “cost-efficacy” of omalizumab from the Swedish payer perspective
• €56,091 per QALY gained
• One drawing on data from pragmatic, observational studies (van Nooten and
colleagues)
• cost-effectiveness of omalizumab from the Netherlands payer perspective
• €38,371 per QALY gained
Campbell JD, McQueen RB, Briggs A. The “E” in Cost-Effectiveness Analyses: A Case Study of Omalizumab Efficacy and Effectiveness for Cost-Effectiveness Analysis Evidence. Ann Am Thorac Soc. 2014 Feb; 11 Suppl 2: S105-11.
Discussion
• Why the continued resistance/slow uptake of using effectiveness data, especially in model-
based cost-effectiveness analyses?
• Time lag between regulatory approval and availability of data from routine clinical practice
• Entrenched positions on the hierarchy of evidence
• Continue educating decision makers on the benefits of effectiveness data for cost-
effectiveness analyses
• Adhere to best practices (eg, ISPOR) or accepted value-based frameworks for cost-effectiveness
research
• Transparency of model technique and results
• Cost-effectiveness and broadly decision analysis do not fit into hierarchy of evidence
• Synthesis of multiple sources of evidence
• Accept that the interpretation of evidence requires judgment*
*Sir Michael Rawlins. De testimonio: on the evidence for decisions about the use of therapeutic interventions. Lancet 2008; 372: 2152-61
Acknowledgements
• Jonathan Campbell, Andrew Briggs
• Contact information: brettmcq@rirl.org

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Using the right E (Efficacy vs. Effectiveness) in Cost-Effectiveness / Affordability Assessments

  • 1. R. Brett McQueen, PhD Health Economist and Head of Statistics Research in Real Life (RiRL)/Observational & Pragmatic Research Institute (OPRI) Adjunct Assistant Professor University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences Using the right ‘E’ (E-fficacy vs. E-ffectiveness) in cost- effectiveness/affordability assessments
  • 2. ISPOR Real-World Task Force • In 2007, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Real-World Data Task Force published a statement in support of using real- world data for coverage and reimbursement decisions • Defined as economic, clinical, or patient-centered data NOT derived from efficacy randomized controlled trials (RCTs) • Pragmatic trials, registries, administrative data, health surveys, electronic records, etc. • Task force called for efficacy and effectiveness to be used to achieve the “fourth hurdle,” demonstration of cost-effectiveness to gain coverage and reimbursement* 2 *Garrison LP Jr, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value Health 2007;10:326–335.
  • 3. Does the drug work and provide good value for money? Randomized Controlled Trials (RCTs) Coverage and Reimbursement bodies: (i.e. Public and Private Payers) Hurdle 4 Cost-Effectiveness Regulatory Bodies: (i.e. US Food and Drug Administration, European Medicines Agency) Hurdles 1–3 Safety, Efficacy, Production RCT-Informed Best Available Evidence Trial-Based CEA Model-Based CEA Can the drug be used safely? Can the drug work? Can it be produced to high quality? End User: Clinicians and Patients Campbell JD, McQueen RB, Briggs A. The “E” in Cost-Effectiveness Analyses: A Case Study of Omalizumab Efficacy and Effectiveness for Cost-Effectiveness Analysis Evidence. Ann Am Thorac Soc. 2014 Feb; 11 Suppl 2: S105-11.
  • 4. Efficacy versus effectiveness data for cost-effectiveness studies • Cost-effectiveness using efficacy • Potential value of treatment tested in ideal environment (e.g., artificially enhanced adherence, protocol-driven resource utilization) • “Piggyback” study or model forecasting trial results • Cost-effectiveness using effectiveness data • Potential value relevant to decision-makers and patients* • Modeling with real-world evidence (e.g., adherence-adjusted treatment effects, real-world resource utilization) • Each can address different yet equally important questions • “Bounds” of evidence for adoption or rejection of a technology§ *Callahan KP, Bridges JF. Using comparative effectiveness research to inform-decision making: is there a role of economic evaluation? Journal of Comparative Effectiveness Research; 1(4), 299-301 (2012). § Campbell JD, McQueen RB, Briggs A. The “E” in Cost-Effectiveness Analyses: A Case Study of Omalizumab Efficacy and Effectiveness for Cost-Effectiveness Analysis Evidence. Ann Am Thorac Soc. 2014 Feb; 11 Suppl 2: S105-11.
  • 5. CEA Steps 1.Define the cost-effectiveness objective, state the perspective of the analysis that will determine what costs are relevant, select the alternative intervention(s), select the study design 2.Define and measure the components of cost (Ci) and effect (Ei) by alternative intervention, 3.Compute the incremental cost-effectiveness ratio, 4.Conduct sensitivity and/or uncertainty analyses, 5.Communicate findings and their limitations.
  • 6. Omalizumab CEA Case Study: Cost-Efficacy versus Cost- Effectiveness: Is There a Difference? • Compare and contrast two European omalizumab cost-effectiveness decision-analytic models: • One drawing on data from explanatory RCT evidence (Dewilde and colleagues) • “cost-efficacy” of omalizumab from the Swedish payer perspective • €56,091 per QALY gained • One drawing on data from pragmatic, observational studies (van Nooten and colleagues) • cost-effectiveness of omalizumab from the Netherlands payer perspective • €38,371 per QALY gained Campbell JD, McQueen RB, Briggs A. The “E” in Cost-Effectiveness Analyses: A Case Study of Omalizumab Efficacy and Effectiveness for Cost-Effectiveness Analysis Evidence. Ann Am Thorac Soc. 2014 Feb; 11 Suppl 2: S105-11.
  • 7. Discussion • Why the continued resistance/slow uptake of using effectiveness data, especially in model- based cost-effectiveness analyses? • Time lag between regulatory approval and availability of data from routine clinical practice • Entrenched positions on the hierarchy of evidence • Continue educating decision makers on the benefits of effectiveness data for cost- effectiveness analyses • Adhere to best practices (eg, ISPOR) or accepted value-based frameworks for cost-effectiveness research • Transparency of model technique and results • Cost-effectiveness and broadly decision analysis do not fit into hierarchy of evidence • Synthesis of multiple sources of evidence • Accept that the interpretation of evidence requires judgment* *Sir Michael Rawlins. De testimonio: on the evidence for decisions about the use of therapeutic interventions. Lancet 2008; 372: 2152-61
  • 8. Acknowledgements • Jonathan Campbell, Andrew Briggs • Contact information: brettmcq@rirl.org