Broekhuizen, H.

Groothuis-Oudshoorn, C.G.M.

Hauber, A.B.

IJzerman, M.J.

English

Broekhuizen, Hindrik

Groothuis-Oudshoorn, Catharina Gerarda Maria

IJzerman, Maarten Joost

University of Twente Research Information

Integrating patient preferences and clinical trial data  in a Bayesian model for benefit-risk assessment H Broekhuizen1, CGM Groothuis1, AB Hauber2 and MJ IJzerman1 (1) University of Twente, the Netherlands (2) RTI Health Solutions, Research Triangle Park, NC 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)  Little known about integrating preferences into assessments 18 Oct 2012 H Broekhuizen SMDM presentation 2 The MCDA model 18 Oct 2012 H Broekhuizen SMDM presentation 3 Clinical trial data Approximation Patient preferences Uncertainty Uncertainty Preference studies Clinical trials Simulation Risk-benefit plane Sensitivity graphs Case study: antidepressants 18 Oct 2012 H Broekhuizen SMDM presentation 4 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 resampling method How is preference information approximated? 18 Oct 2012 H Broekhuizen SMDM presentation 5 0%10%20%30%40%50%60%70%80%90%100%1 2 3 4 5 6 7 8 9 10 11 12Weight Respondent Adverse EventsRemissionResponsRespons weightFrequency (total=1000)0.45 0.50 0.55 0.60 0.65 0.70 0.75050100150 Approximated by a bootstrap resampling method Case study: antidepressants 18 Oct 2012 H Broekhuizen SMDM presentation 6 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 placebo How is clinical data approximated? 18 Oct 2012 H Broekhuizen SMDM presentation 7 Duloxetine Venlafaxine Bupropion 0.5 1.0 2.0 5.002468OR compared to placebo Approximated by a normal distribution in the log domain Bupropion remission performanceOR compared to placeboFrequency (total=1000)1.0 1.2 1.4 1.6 1.8 2.0020406080100120Remission OR (Bupr pion compared to placebo)  For a particular drug i in simulation run t,  𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠𝑖,𝑡 =𝑊𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑡𝑊𝑟𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡⋅𝑂𝑅𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑖,𝑡𝑂𝑅𝑟𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑡,𝑖  𝑅𝑖𝑠𝑘𝑠𝑖,𝑡 = 𝑊𝐴𝐸𝑡 ⋅ 𝑂𝑅𝐴𝐸𝑖,𝑡  𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑅𝑖𝑠𝑘𝑅𝑎𝑡𝑖𝑜𝑖,𝑡 = 𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠𝑖,𝑡 𝑅𝑖𝑠𝑘𝑠𝑖,𝑡    Benefits and risks plotted in risk-benefit plane  Integration of patient preferences and clinical data 18 Oct 2012 H Broekhuizen SMDM presentation 8 Risk-benefit plane 18 Oct 2012 H Broekhuizen SMDM presentation 9 μ=1 line Weighted 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) Comparing benefits and risks in the risk-benefit plane Risk-benefit plane 18 Oct 2012 10 0.0 0.5 1.0 1.5 2.00.00.20.40.60.81.0Weighted benefits (odds ratio compared to plabebo)Weighted risks (odds ratio compared to placebo)Bupropion Venlafaxine Duloxetine μ=1 line Weighted risks (OR) Weighted benefits (OR) H Broekhuizen SMDM presentation 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  Insight for further research   18 Oct 2012 H Broekhuizen SMDM presentation 11 0.15 0.20 0.25 0.3001234567Adverse events weightDuloxetine Benefit-risk ratioValue sensitivity 18 Oct 2012 12 95% CI Duloxetine benefit-risk ratio Adverse v nt  weight H Broekhuizen SMDM presentation μ=1 μ=3 0.10 0.15 0.20 0.25 0.30 0.350.00.20.40.60.81.0Decision sensitivity 18 Oct 2012 H Broekhuizen SMDM presentation 13 95% CI Adverse events weight P(Duloxetine acceptable) at μ=3 1.5 2.0 2.5 3.00.00.20.40.60.81.0Venlafaxine  rank probabilityRanking sensitivity 18 Oct 2012 H Broekhuizen SMDM presentation 14 Rank reversal P(rank=1) P(rank=2) P(rank=3) Venlafaxine response performance (OR compared to placebo) Probability 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  Model cannot make decisions, only assist regulators 18 Oct 2012 H Broekhuizen SMDM presentation 16 Thank you           Email: h.broekhuizen@utwente.nl  Web:   www.utwente.nl/mb/htsr/Staff/broekhuizen.doc/  18 Oct 2012 H Broekhuizen SMDM presentation 17 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 information,” Medical Decision Making, vol. 18, no. 1, pp. 95–109, 1998.  18 Oct 2012 H Broekhuizen SMDM presentation 18 

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

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

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

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