Implementation of a Bayesian adaptive design in a proof of concept study

Pharm Stat. Jan-Mar 2006;5(1):39-50. doi: 10.1002/pst.198.

Abstract

With increased costs of drug development the need for efficient studies has become critical. A key decision point on the development pathway has become the proof of concept study. These studies must provide clear information to the project teams to enable decision making about further developing a drug candidate but also to gain evidence that any effect size is sufficient to warrant this development given the current market environment. Our case study outlines one such proof of concept trial where a new candidate therapy for neuropathic pain was investigated to assess dose-response and to evaluate the magnitude of its effect compared to placebo. A Normal Dynamic Linear Model was used to estimate the dose-response--enforcing some smoothness in the dose-response, but allowing for the fact that the dose-response may be non-monotonic. A pragmatic, parallel group study design was used with interim analyses scheduled to allow the sponsor to drop ineffective doses or to stop the study. Simulations were performed to assess the operating characteristics of the study design. The study results are presented. Significant cost savings were made when it transpired that the new candidate drug did not show superior efficacy when compared placebo and the study was stopped.

MeSH terms

  • Bayes Theorem*
  • Dose-Response Relationship, Drug
  • Double-Blind Method
  • Humans
  • Linear Models
  • Neuralgia, Postherpetic / drug therapy*
  • Randomized Controlled Trials as Topic / methods*
  • Research Design*