INHANCE: An Adaptive Confirmatory Study with Dose Selection at Interim

The Acute Stroke Therapy by Inhibition of Neutrophils study - Key features and impact

The Acute Stroke Therapy by Inhibition of Neutrophils (ASTIN) study, initiated in November of the year 2000, is now widely recognized as having been a landmark study in the history of clinical trials. We look at why this is the case by considering its key features and impact. These key features are: the use of Bayesian design and analysis; the use of the normal dynamic linear model; the response adaptive nature of the study; the use of real-time dosing decisions; and the use of an integrated model to predict 90-day response on the Scandinavian Stroke Scale. Our overall conclusion is that the ASTIN study's main impact came from showing the clinical trial community the feasibility of the novel design and analysis used when most of these key features were rarely used in industry trials, let alone used together in one trial in a disease area with a tremendous unmet medical need.

Conditionally unbiased estimation in the normal setting with unknown variances

To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (Statistics & Probability Letters, 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance. Our extension allows for multiple selected candidates, as well as unequal stage one and two sample sizes.

Adaptive designs: The Swiss Army knife among clinical trial designs?

There has been considerable progress in the development and implementation of adaptive designs over the past 30 years. A major driver for this class of novel designs is the possibility to increase the information value of clinical trial data to enable better decisions, leading to more efficient drug development processes and improved late-stage success rates. In the first part of this article, we review the development of adaptive designs from different perspectives. We trace back key historical papers, report on landmark adaptive design clinical trials, review major cross-industry collaborations, and highlight key regulatory guidance documents. In the second, more technical part of this article, we address the question of whether it is possible to define factors which guide the choice between a fixed or an adaptive design for a given trial. We show that in non-linear regression models with a moderate variance of the responses, the first-stage sample size of an adaptive design should be chosen sufficiently large in order to address variability in the interim parameter estimate. In conclusion, the choice between an adaptive and a fixed design depends in a sensitive manner on the specific statistical problem under investigation.

Designing multi-arm multi-stage clinical trials using a risk-benefit criterion for treatment selection

Multi‐arm clinical trials that compare several active treatments to a common control have been proposed as an efficient means of making an informed decision about which of several treatments should be evaluated further in a confirmatory study. Additional efficiency is gained by incorporating interim analyses and, in particular, seamless Phase II/III designs have been the focus of recent research. Common to much of this work is the constraint that selection and formal testing should be based on a single efficacy endpoint, despite the fact that in practice, safety considerations will often play a central role in determining selection decisions. Here, we develop a multi‐arm multi‐stage design for a trial with an efficacy and safety endpoint. The safety endpoint is explicitly considered in the formulation of the problem, selection of experimental arm and hypothesis testing. The design extends group‐sequential ideas and considers the scenario where a minimal safety requirement is to be fulfilled and the treatment yielding the best combined safety and efficacy trade‐off satisfying this constraint is selected for further testing. The treatment with the best trade‐off is selected at the first interim analysis, while the whole trial is allowed to compose of J analyses. We show that the design controls the familywise error rate in the strong sense and illustrate the method through an example and simulation. We find that the design is robust to misspecification of the correlation between the endpoints and requires similar numbers of subjects to a trial based on efficacy alone for moderately correlated endpoints. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

A simple and flexible graphical approach for adaptive group-sequential clinical trials

In this article, we introduce a graphical approach to testing multiple hypotheses in group-sequential clinical trials allowing for midterm design modifications. It is intended for structured study objectives in adaptive clinical trials and extends the graphical group-sequential designs from Maurer and Bretz (Statistics in Biopharmaceutical Research 2013; 5: 311-320) to adaptive trial designs. The resulting test strategies can be visualized graphically and performed iteratively. We illustrate the methodology with two examples from our clinical trial practice. First, we consider a three-armed gold-standard trial with the option to reallocate patients to either the test drug or the active control group, while stopping the recruitment of patients to placebo, after having demonstrated superiority of the test drug over placebo at an interim analysis. Second, we consider a confirmatory two-stage adaptive design with treatment selection at interim.