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

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.     

Simulation-based sequential Bayesian design

We consider simulation-based methods for exploration and maximization of expected utility in sequential decision problems. We consider problems which require backward induction with analytically intractable expected utility integrals at each stage. We propose to use forward simulation to approximate the integral expressions, and a reduction of the allowable action space to avoid problems related to an increasing number of possible trajectories in the backward induction. The artificially reduced action space allows strategies to depend on the full history of earlier observations and decisions only indirectly through a low dimensional summary statistic. The proposed rule provides a finite-dimensional approximation to the unrestricted infinite-dimensional optimal decision rule. We illustrate the proposed approach with an application to an optimal stopping problem in a clinical trial.     

Strategy—The key to planning in the public corporation

The distinctive features and constraints of planning in the nationalized industries as compared with large firms in the private sector deserve more attention than they have received. The development of corporate planning in these industries has tended to take a different route, starting from the need for long term physical planning. Planning in nationalized industries with complex production systems has a good deal in common with planning in large integrated firms (as opposed to conglomerates). But the difficulties arising from present methods of government control might be eased by placing greater emphasis on the need for stable agreement with the government on the industries' broad strategies.     

On implementing Jeffreys' substitution likelihood for Bayesian inference concerning the medians of unknown distributions

When statisticians are uncertain as to which parametric statistical model to use to analyse experimental data, they will often resort to a non-parametric approach. The purpose of this paper is to provide insight into a simple approach to take when it is unclear as to the appropriate parametric model and plan to conduct a Bayesian analysis. I introduce an approximate, or substitution likelihood, first proposed by Harold Jeffreys in 1939 and show how to implement the approach combined with both a non-informative and an informative prior to provide a random sample from the posterior distribution of the median of the unknown distribution. The first example I use to demonstrate the approach is a within-patient bioequivalence design and then show how to extend the approach to a parallel group design.