mcda in drug benefit-risk analysis: the case of second-generation antidepressants
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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
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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
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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
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European Conference on Operational Research 2009, Bonn
0 2 4 6 8 10 12
relative risk
0.0
0.1
0.2
0.3
0.4
Sampling distribution
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European Conference on Operational Research 2009, Bonn
0.5 1.0 1.5 2.0 2.5 3.0
relative efficacy
0
2
4
6
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rela
tive
risk
NE
SE
NW
SW
Benefit-risk plane (combining benefit and risk)
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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)
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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
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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]
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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
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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!
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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
10
15
20
25
Efficacy Diarrhea Dizziness Headache Insomnia Nausea
%
Fluoxetine
Paroxetine
Sertraline
Venlafaxine
CF
49%
45%
36%
74%
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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
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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
0.10
0.20
0.30
0.40
0.50
0.60
Acc
Rank
1
2
3
4
Fluoxetine
Paroxetine
Sertraline
Venlafaxine
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
Acc
Rank
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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)
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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
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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
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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