Multi-criteria decision analysis in drug benefit-risk assessment

1. Multi-criteria decision analysis in drug benefit-risk assessment T. Tervonen(1), D. Postmus(2), H.L. Hillege(3) 1 Faculty of Economics and Business, RUG.nl 2 Department of Epidemiology, UMCG.nl 3 Department of Cardiology/Epidemiology, UMCG.nl

2. Introduction • Drug Benefit-Risk (BR) analysis aims to systemically compare the benefits and risks of drugs within a therapeutic group • BR analysis has multiple possible applications • Support prescription decisions • One criterion for drug marketing authorization decision (in Europe, FDA in USA doesn’t give incorporate BR analysis in clinical assessment)

3. Two ways to approach BR analysis • Universal model • Becomes too general • Explicitly requires qualitative measurements • Hard for MD’s to accept • Doesn’t show the potential of MCDA • Therapeutic group specific model • Allows to take into account quantitative clinical data • The model can be discussed with leading experts of the therapeutic area • Separates qualitative judgments from clinical data

4. Clinical data Therapeuticgroup Drug 1 Drug 2 Drug 3 Study 1 Study 2 Study 3 Study 4 Endpoint A Endpoint b Endpoint c

5. SMAA approach to BR analysis • Step 1: Analyze without preference information to characterize the drugs • Step 2: Analyze through common scenarios including ordinal preferences obtained from expert MD’s • Justification for SMAA: • Allows missing/incomplete preferences • Gaussian distributed criteria values • is based on MAUT

6. Example • 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

7. 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

8. Measurements (mean, stdev)

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

10. SMAA analysis without preferences: central weights and confidence factors • Can be used in describing the most preferred drug taking into account the patient history CF 46% 53% 34% 68%

11. 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

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

13. 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 • The problem statement is not “choice” or “ranking”, but “risk assessment” Merci ! 