Econometrics Pharmaceuticals

Longer trials or larger sample size?

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.


APPENDIX: Steps for simulating the model

  1. Specify a series of possible trial designs Ψ, for example, sample size n, duration, etc.
  2. Draw realisation of each parameter from its prior distribution.
  3. Generate a sample of patients in the trial and randomly assign them to treatments 1 and 2.
  4. Simulate the clinical trial result inline image using sampled parameters.
  5. Select patients for analysis according to trial design.
  6. Estimate a value-based price P* given the sample data inline image.
  7. Repeat steps 5–7 for all design options.
  8. Repeat steps 1–8 for 10 000 iterations.
  9. Evaluate the total profit forecast for each simulated trial.
  10. 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).

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