Integrating patient preferences and clinical trial data in a Bayesian model for benefit-risk assessment

Integrating patient preferences and clinical trial data

in a Bayesian model for benefit-risk assessment

H Broekhuizen1_{, CGM Groothuis}1_{, AB Hauber}2 _{and MJ IJzerman}1

(1) University of Twente, the Netherlands

Patient preferences in benefit-risk assessment

 Patient preferences matter

 They experience the benefits and risks

 Preferences can differ between regulators and patients  Example: Natalizumab case

 Growing interest (FDA, EMA)

The MCDA model

Clinical trial data Approximation Patient preferences Uncertainty Uncertainty Preference

studies Clinical trials

Simulation

Case study: antidepressants

Patient preferences Uncertainty Preference studies  Preferences: Analytical

Hierarchy Process study by Danner et al. (2011)

 Respondents: 12 MDD patients

 Benefit criteria: response and remission

 Risk criterion: adverse events (low and high severity)

 Benefit and risk outcomes assumed to be independent  Approximated by a bootstrap

How is preference information approximated?

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 2 3 4 5 6 7 8 9 10 11 12 W eigh t Respondent Adverse Events Remission Respons Respons weight F re q u e n cy (t o ta l= 1 0 0 0 ) 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0 50 100 150  Approximated by a bootstrap resampling method

Case study: antidepressants

Clinical trial data Uncertainty Clinical trials  Performance: Systematic

review by German Institute for Quality and Efficiency in

Health Care (IQWIG)  Drugs: Duloxetine,

Venlafaxine and Bupropion  Odds ratio compared to

How is clinical data approximated?

Dulo x e tin e V e n laf a x ine B u p rop io n 0.5 1.0 2.0 5.0 0 2 4 6 8 OR compared to placebo  Approximated by a normal distribution in the log domain

Bupropion remission performance

OR compared to placebo F re q u e n cy (t o ta l= 1 0 0 0 ) 1.0 1.2 1.4 1.6 1.8 2.0 0 20 40 60 80 100 120

 For a particular drug i in simulation run t, 𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠_𝑖,𝑡 = _𝑊𝑊𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑡 𝑟𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡 ⋅ 𝑂𝑅_{𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑖,𝑡} 𝑂𝑅_{𝑟𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡,𝑖} 𝑅𝑖𝑠𝑘𝑠_𝑖,𝑡 = 𝑊_𝐴𝐸𝑡 ⋅ 𝑂𝑅_{𝐴𝐸𝑖,𝑡} 𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑅𝑖𝑠𝑘𝑅𝑎𝑡𝑖𝑜_𝑖,𝑡 = 𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠_𝑖,𝑡 𝑅𝑖𝑠𝑘𝑠_𝑖,𝑡

 Benefits and risks plotted in risk-benefit plane

Risk-benefit plane

μ=1 line W ei gh ted risks (OR)

Weighted benefits (OR)

 μ is decision threshold, μ=1 requires

drugs to have >1 weighted benefit for each weighted risk to be acceptable, i.e:

 Benefit-risk-ratio>μ  benefits outweigh risks

 Percentage points under line approximates P(acceptable)

Risk-benefit plane

0.0 0.5 1.0 1.5 2.0 0 .0 0 .2 0 .4 0 .6 0 .8 1 .0 W e ig h te d r isks (o d d s ra ti o co m p a re d t o p la ce b o ) Bupropion Venlafaxine Duloxetine μ=1 line W ei gh ted risks (OR)

Sensitivity

 What is the impact of uncertainty surrounding model parameters  Important distinction (Felli 1998)

 Value sensitivity (change in expected value)

 Decision sensitivity (change in decision, i.e. other drug chosen)  Ranking sensitivity (change in rank order of drugs)

 Why would we want to know?  Robustness

 Heterogeneity

0.15 0.20 0.25 0.30 0 1 2 3 4 5 6 7 D u lo xe ti n e B e n e fi t-ri sk ra ti o

Value sensitivity

95% CI Du lo x etin e be ne fit -risk ratio

Adverse events weight

μ=1 μ=3

0.10 0.15 0.20 0.25 0.30 0.35 0 .0 0 .2 0 .4 0 .6 0 .8 1 .0

Decision sensitivity

95% CI

Adverse events weight

P( Du lo x etin e acce pta bl e ) at μ =3

1.5 2.0 2.5 3.0 0 .0 0 .2 0 .4 0 .6 0 .8 1 .0 V e n la fa xi n e ra n k p ro b a b il it y

Ranking sensitivity

Rank reversal P(rank=1) P(rank=2) P(rank=3)

Venlafaxine response performance (OR compared to placebo)

Prob

ab

A B Parameter 1 Parameter 2 Parameter 4 Parameter 5 Parameter 3 Parameter 6 80% 20%

Discussion

 Integration preferences and performance  Impact uncertainty can be assessed

 Visual representations can help regulators and enrich the discussion during the benefit-risk assessment proces.  Assumptions in antidepressants case

 Simplified structure  Independence

 What probability is convincing?

 Other methods needed to check external validity

Thank you

 Email: h.broekhuizen@utwente.nl

References

 M. Danner, J. M. Hummel, F. Volz, J. G. van Manen, B. Wiegard, C.-M. Dintsios, H. Bastian, A. Gerber, and M. J. Ijzerman, “Integrating patients’ views into health technology assessment: Analytic hierarchy process (AHP) as a method to elicit patient preferences.,” International journal of

technology assessment in health care, vol. 27, no. 4, pp. 369–75, Oct.

2011.

 “Selective serotonin and noradrenalin reuptake inhibitors (SNRI) with

depression patients [Selektive Serotonin- und Wiederaufnahmehemmer (SNRI) bei Patienten mit Depressionen],” Cologne, 2010.

 J. Felli, “Sensitivity analysis and the expected value of perfect