Adaptive dose-response studies to establish proof-of-concept in learning-phase clinical trials

TL;DR

This paper develops adaptive dose-response testing methods for early-phase clinical trials, improving efficiency and proof-of-concept detection by using interim data to adapt models and doses, with controlled error rates.

Contribution

It extends MCP-Mod methodology with GMCTs and CRP principles to two-stage adaptive designs for more flexible and informative proof-of-concept studies.

Findings

Adaptive designs outperform non-adaptive ones when model selection is uncertain.

Simulation shows improved power and error control in adaptive dose-response testing.

Methods effectively combine stage-wise p-values and t-statistics for robust inference.

Abstract

In learning-phase clinical trials in drug development, adaptive designs can be efficient and highly informative when used appropriately. In this article, we extend the multiple comparison procedures with modeling techniques (MCP-Mod) procedure with generalized multiple contrast tests (GMCTs) to two-stage adaptive designs for establishing proof-of-concept. The results of an interim analysis of first-stage data are used to adapt the candidate dose-response models and the dosages studied in the second stage. GMCTs are used in both stages to obtain stage-wise p-values, which are then combined to determine an overall p-value. An alternative approach is also considered that combines the t-statistics across stages, employing the conditional rejection probability (CRP) principle to preserve the Type I error probability. Simulation studies demonstrate that the adaptive designs are advantageous compared to the corresponding tests in a non-adaptive design if the selection of the candidate set of dose-response models is not well informed by evidence from preclinical and early-phase studies.