Optimizing Trial Designs for Targeted Therapies

TL;DR

This paper develops a decision-theoretic framework to optimize clinical trial designs for targeted therapies, balancing sample size, population selection, and testing procedures to maximize utility considering costs and benefits.

Contribution

It introduces a novel approach that incorporates prior knowledge and utility functions to optimize trial parameters for targeted therapies.

Findings

Optimized trial designs improve efficiency and decision-making.

The approach accounts for both sponsor and public health perspectives.

Numerical examples demonstrate practical application of the method.

Abstract

An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor's as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives.