Seamless phase II/III clinical trials using early outcomes for treatment or subgroup selection: Methods and aspects of their implementation

TL;DR

This paper introduces a unified framework and simulation tools for adaptive seamless phase II/III clinical trials that utilize early outcomes for treatment or subgroup selection, enhancing trial efficiency.

Contribution

It presents a comprehensive framework linking treatment and subgroup selection methods and extends the R package asd for adaptive enrichment design simulations.

Findings

Framework links treatment and subgroup selection literature

Extended R package asd for adaptive enrichment designs

Illustrative examples in COPD and oncology trials

Abstract

Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, e.g. in terms of sample size and / or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in form of extensive simulation studies, than conventional approaches. Here we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and yet efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate both, interim analyses informed by the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subpopulation selection (so-called adaptive enrichment designs). Here we describe the functionality of the R package asd and use it to present some worked-up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples illustrate various features of the R package providing at the same time insights into the operating characteristics of adaptive seamless studies.