Generating the right evidence at the right time: Principles of a new class of flexible augmented clinical trial designs

TL;DR

The paper introduces FACTIVE, a novel flexible trial design that combines randomized controlled trials with real-world data to generate timely, relevant evidence for multiple stakeholders in drug development.

Contribution

It proposes a new class of study designs enabling integration of RCTs with real-world data, improving evidence relevance and timing for decision-making.

Findings

FACTORI design facilitates early evidence generation for HTA bodies.

High-quality data are obtained within a single protocol using randomization.

The approach allows estimation of different treatment effects in separate trial components.

Abstract

The past few years have seen an increasing number of initiatives aimed at integrating information generated outside of confirmatory randomised clinical trials (RCTs) into drug development. However, data generated non-concurrently and through observational studies can provide results that are difficult to compare with randomised trial data. Moreover, the scientific questions these data can serve to answer often remain vague. Our starting point is to use clearly defined objectives for evidence generation, which are formulated towards early discussion with health technology assessment (HTA) bodies and are additional to regulatory requirements for authorisation of a new treatment. We propose FACTIVE (Flexible Augmented Clinical Trial for Improved eVidencE generation), a new class of study designs enabling flexible augmentation of confirmatory randomised controlled trials with concurrent and close-to-real-world elements. These enabling designs facilitate estimation of certain treatment effects in the confirmatory part and other, complementary treatment effects in a concurrent real-world part. Each stakeholder should use the evidence that is relevant within their own decision-making framework. High quality data are generated under one single protocol and the use of randomisation ensures rigorous statistical inference and interpretation within and between the different parts of the experiment. Evidence for the decision-making of HTA bodies could be available earlier than is currently the case.