Developing drugs is expensive. Some estimates have estimated that the cost of bringing a drug to market is $1 billion. In addition, payers are now reimbursing based on the perceived value of a treatment. That is, treatments that provide more health benefits receive higher reimbursements.
In this world of value-based pricing (VBP), pharmaceutical companies have an incentive to show strong efficacy not only to secure approval, but also to secure higher reimbursement rates from payers. A question for these firms is, should they invest in trials with a longer duration or in increasing the trials’ sample size?
A paper by Breeze and Brennan (2014) relies on the following methodology to answer this question:
We modify the traditional framework for conducting ENBS [expected net benefit of sampling] and assume that the price of the drug is conditional on the trial outcomes. We use a value-based pricing (VBP) criterion to determine price conditional on trial data using Bayesian updating of cost-effectiveness (CE) model parameters.
Using this approach and parameterizing the model using treatments for systemic lupus erythematosus, the authors find that:
…shorter trials with a large sample size are associated with greater profit forecasts for the pharmaceutical company. Although there is substantial uncertainty in the long-term effectiveness of treatments in chronic diseases, increasing sample size is a more efficient method of data collection in this illustrative example.
- Breeze P. and Brennan A., (2014), VALUING TRIAL DESIGNS FROM A PHARMACEUTICAL PERSPECTIVE USING VALUE-BASED PRICING, Health Economics. DOI: 10.1002/hec.3103
APPENDIX: Steps for simulating the model
- Specify a series of possible trial designs Ψ, for example, sample size n, duration, etc.
- Draw realisation of each parameter from its prior distribution.
- Generate a sample of patients in the trial and randomly assign them to treatments 1 and 2.
- Simulate the clinical trial result using sampled parameters.
- Select patients for analysis according to trial design.
- Estimate a value-based price P* given the sample data .
- Repeat steps 5–7 for all design options.
- Repeat steps 1–8 for 10 000 iterations.
- Evaluate the total profit forecast for each simulated trial.
- Evaluate the ECNB across all simulated trials for each trial design and identify the trial design that has the optimal value (i.e. highest ECNB).