European Conference on Operational Research 2009, Bonn

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.nl2 Department of Cardiology/Epidemiology, UMCG.nl3 Department of Epidemiology, UMCG.nl

European Conference on Operational Research 2009, Bonn

>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

Benefit-risk assessment

Data and evidence

Regulatory Logic

European Conference on Operational Research 2009, Bonn

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

TreatmentControlBenefit

European Conference on Operational Research 2009, Bonn

0.0 0.5 1.0 1.5 2.0 2.5 3.0

relative efficacy

0.0

0.5

1.0

1.5

Sampling distribution

European Conference on Operational Research 2009, Bonn

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

Control Treatment

Risk

European Conference on Operational Research 2009, Bonn

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relative risk

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Sampling distribution

European Conference on Operational Research 2009, Bonn

0.5 1.0 1.5 2.0 2.5 3.0

relative efficacy

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rela

tive

risk

NE

SE

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SW

Benefit-risk plane (combining benefit and risk)

European Conference on Operational Research 2009, Bonn

>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)

European Conference on Operational Research 2009, Bonn

>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

European Conference on Operational Research 2009, Bonn

> 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

Name Measurements Pref. dir. Scale

EfficacyRelative value compared

with Fluoxetine↑ [0.97, 1.23]

Diarrhea ADE’s

Absolute % ↓ [1, 20.6]

Dizziness ADE’s

Absolute % ↓ [2.9, 24.4]

Headache ADE’s

Absolute % ↓ [8, 31.3]

Insomnia ADE’s

Absolute % ↓ [3.4, 21.3]

Nausea ADE’s

Absolute % ↓ [22.1, 34]

European Conference on Operational Research 2009, Bonn

>Measurements (mean, stdev)

Drug Ln(Efficacy) Diarrhea Dizziness Headache Insomnia Nausea

Fluoxetine 0, 0 11.7, 2.5 7.2, 1.45 16.6, 3.27 13.7, 1.89 18.6, 1.79

Paroxetine 0.086, 0.056 9.2, 1.86 10.6, 1.58 21.2, 5.15 14.3, 2.93 18.3, 3.7

Sertraline 0.095, 0.044 15.4, 2.65 7.5, 1.48 20.2, 3.78 15, 3.21 19.5, 2.6

Venlafaxine 0.113, 0.048 5.5, 2.32 15.7, 4.44 12.8, 2.45 11.2, 3.98 31, 1.68

European Conference on Operational Research 2009, Bonn

>Measurements (mean, stdev)

Drug Ln(Efficacy) Diarrhea Dizziness Headache Insomnia Nausea

Fluoxetine 0, 0 11.7, 2.5 7.2, 1.45 16.6, 3.27 13.7, 1.89 18.6, 1.79

Paroxetine 0.086, 0.056 9.2, 1.86 10.6, 1.58 21.2, 5.15 14.3, 2.93 18.3, 3.7

Sertraline 0.095, 0.044 15.4, 2.65 7.5, 1.48 20.2, 3.78 15, 3.21 19.5, 2.6

Venlafaxine 0.113, 0.048 5.5, 2.32 15.7, 4.44 12.8, 2.45 11.2, 3.98 31, 1.68

Not asignificantdifference!

European Conference on Operational Research 2009, Bonn

> 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

0

5

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15

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25

Efficacy Diarrhea Dizziness Headache Insomnia Nausea

%

Fluoxetine

Paroxetine

Sertraline

Venlafaxine

CF

49%

45%

36%

74%

European Conference on Operational Research 2009, Bonn

>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

European Conference on Operational Research 2009, Bonn

>SMAA analyses with preferences: rank acceptabilities

>Can be used for scenario-based prescription

Mild depressionSevere depression

12

34

Fluoxetine

Paroxetine

Sertraline

Venlafaxine

0.00

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0.20

0.30

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0.60

Acc

Rank

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Fluoxetine

Paroxetine

Sertraline

Venlafaxine

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Acc

Rank

European Conference on Operational Research 2009, Bonn

>How to get from being useful to be usable?

>JSMAA Minimum user interaction (automatic scale

computation, multi-threaded simulation)

European Conference on Operational Research 2009, Bonn

>How to get from being useful to be usable?

>ADDIS Storage and

meta-analysis of aggregate clinical data

European Conference on Operational Research 2009, Bonn

>How to get from being useful to be usable?

>ADDIS + JSMAA (Semi) automatic model construction from

aggregate clinical data

European Conference on Operational Research 2009, Bonn

>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