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MCDA in drug benefit-risk analysis: the case of second-generation antidepressants

MCDA in drug benefit-risk analysis: the case of second-generation antidepressants. T. Tervonen(1), H.L. Hillege(2), D. Postmus(3) 1 Faculty of Economics and Business, RUG.nl 2 Department of Cardiology/Epidemiology, UMCG.nl 3 Department of Epidemiology, UMCG.nl. Regulatory Logic.

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MCDA in drug benefit-risk analysis: the case of second-generation antidepressants

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  1. MCDA in drug benefit-risk analysis: the case of second-generation antidepressants T. Tervonen(1), H.L. Hillege(2), D. Postmus(3) 1 Faculty of Economics and Business, RUG.nl 2 Department of Cardiology/Epidemiology, UMCG.nl 3 Department of Epidemiology, UMCG.nl

  2. Regulatory Logic Benefit-risk assessment Data and evidence • Introduction • Drug Benefit-Risk (BR) analysis aims to systemically compare the benefits and risks of drugs within a therapeutic group • Benefit and risk criteria are often evaluated separately from each other • Focus on statistical significance(p < 0.05) • Scope: drug approval (high in EMEA list) and prescription decisions

  3. Benefit Control Treatment Relative efficacy: (63/100) / (57/100) = 1.11 (0.70 – 1.74),p = 0.33 (one-sided)

  4. Sampling distribution

  5. Risk Control Treatment Relative risk: (14/100) / (7/100) = 2 (0.77 – 5.16), p = 0.08 (one-sided)

  6. Sampling distribution

  7. Benefit-risk plane (combining benefit and risk) NW NE SW SE

  8. Frequentist perspective: the results are inconclusive • The null hypothesis that the two drugs have the same benefit-risk profile cannot be rejected • Bayesian decision perspective: there is a high probability that the treatment is both more effective and more risky • Should the new drug be subscribed / approved to the market? • We go with SMAA (can handle log-normal distributed measurements)

  9. Our case • Therapeutic group: Second-generation anti-depressants • Drugs: • Fluoxetine (Prozac) • Paroxetine (Seroxat) • Sertraline (Zoloft) • Venlafaxine (Effexor) • Purpose: Analyze trade-offs based on clinical data to support prescription decision for two scenarios: • Mild depression • Severe depression

  10. 1 benefit criterion (efficacy), a primary endpoint in studies of the 4 drugs • 5 risk criteria corresponding to the 5 most frequent adverse drug events • Measurements from meta-analysis that pooled results of compatible studies

  11. Measurements (mean, stdev)

  12. Not asignificantdifference! • Measurements (mean, stdev)

  13. SMAA analysis without preferences: central weights and confidence factors (rank acceptabilities showed reasonable rank profiles for all drugs) • Can be used in describing the most preferred drug taking into account the patient history CF 49% 45% 36% 74%

  14. Ordinal preferences • Expert in the field of anti-depressants could understand the model and rank the criteria swings during a short teleconference (30min) • Two rankings for the two scenarios: • Mild depression: Diarrhea > Nausea > Dizziness > Insomnia > Headache > Efficacy • Severe depression: Similar ranking, except efficacy the most important criterion • Ranking took into account swings, and was justified through clinical practice

  15. SMAA analyses with preferences: rank acceptabilities • Can be used for scenario-based prescription Mild depression Severe depression

  16. How to get from being useful to be usable? • JSMAA • Minimum user interaction (automatic scale computation, multi-threaded simulation)

  17. How to get from being useful to be usable? • ADDIS • Storage and meta-analysis of aggregate clinical data

  18. How to get from being useful to be usable? • ADDIS + JSMAA • (Semi) automatic model construction from aggregate clinical data

  19. Conclusions • We constructed a therapeutic group specific SMAA model for benefit-risk assessment of second-generation anti-depressants • Separation of clinical data from preferences gives “credibility” to the model • From useful to usable through open-source software • www.drugis.org • www.smaa.fi

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