ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

A Research Framework Based on Modeling and Simulation for Expected Net Benefits and Making Decisions on Willingness to Pay per Health Gain when Conducting Randomized Clinical Trials

11 September 2017, 16:20 – 16:40


Submitted by
Ismail Abbas
Ismail Abbas (Universitat Politecnica de Catalunya)
Introduction: The expected net benefits of clinical trials depend on willingness to pay per health gain, and the aim is to show what amount of willingness to pay should be attached to health benefits when testing the hypothesis that there is or not a statistical evidence of ENB-expected net benefit.

Methods: The framework considers two-stages modeling and simulation with prior information of a clinical trial. The first-stage calculates sample size and power for testing a primary hypothesis of the trial. Giving the resulting power and sample size, the hypothesis of ENB is tested to make decision on power and willingness to pay per health gain.

Results: The framework is populated with a clinical trial data on benefits including efficacy, effectiveness and health care cost, combined with an assumption on correlated distributions, market exclusivity period and trial cost. The trial compares the magnetic resonance imaging-MRI with computerized axial tomography scanner-CT interventions in the diagnostic of acute ischemic stroke, assessing that CT is the dominant intervention by means of cost-effectiveness analysis. The ENB is €26M for 0.8 (80%) of power, at which €41000 should be attached per unit of health benefits. Sensitivity-analyses results and optimal ENB for a given willingness to pay will also be presented.

Discussion and conclusions: Simulation modeling is useful when making decision on a reasonable willingness to pay per health gain for randomized clinical trials that incorporate expected net benefits analysis. Nonetheless, the framework might be considered as a base for further developments and applications.
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