1,721,032 research outputs found
Augmented binary method for basket trials (ABBA)
Full text link\ua9 The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).In several clinical areas, traditional clinical trials often use a responder outcome, a composite endpoint that involves dichotomising a continuous measure. An augmented binary method that improves power while retaining the original responder endpoint has previously been proposed. The method leverages information from the undichotomised component to improve power. We extend this method for basket trials, which are gaining popularity in many clinical areas. For clinical areas where response outcomes are used, we propose the new augmented binary method for basket trials that enhances efficiency by borrowing information on the treatment effect between subtrials. The method is developed within a latent variable framework using a Bayesian hierarchical modelling approach. We investigate the properties of the proposed methodology by analysing point estimates and high-density intervals in various simulation scenarios, comparing them to the standard analysis for basket trials that assumes binary outcomes. Our method results in a reduction of 95% high-density interval of the posterior distribution of the log odds ratio and an increase in power when the treatment effect is consistent across subtrials. We illustrate our approach using real data from two clinical trials in rheumatology
Recent developments in group-sequential designs
No full text\ua9 2014 Springer-Verlag Berlin Heidelberg. All rights are reserved.In a group-sequential trial, patients are recruited in groups, and their response to treatment is assessed. After each group is assessed, an interim analysis is conducted. At each interim analysis, the trial can stop for futility, stop for efficacy, or continue. The main advantage of group-sequential designs is that the expected number of patients is reduced compared to a design without interim analyses. There are infinitely many possible group-sequential designs to use, and the choice strongly affects the operating characteristics of the trial. This chapter discusses optimal and admissible group-sequential designs. Optimal designs minimise the expected sample size at some specified treatment effect; admissible designs optimise a weighted sum of trial properties of interest, such as expected sample size and maximum sample size. Methods for finding such designs are discussed, including a detailed description of an R package that implements a quick search procedure. Recent applications of group-sequential methodology to trials with multiple experimental treatments being tested against a single control treatment are also described
Discussion on "Adaptive enrichment designs with a continuous biomarker" by Nigel Stallard
No full textReducing the average number of patients needed in a phase II trial through novel design
No full textPhase II represents a very important part of the drug development process. It is important that genuinely effective treatments have a high chance of succeeding whilst treatments that will fail at phase III are screened out. Because of the high number of treatments available for testing and limited resources and patients available, it is increasingly of interest to apply novel designs to improve the efficiency of phase II trials. This paper shall argue that phase II presents the most promising area for applying novel designs and will review some recent developments in three classes of novel design: group-sequential designs, multi-arm designs, and enrichment designs. All three types of design considerably improve the efficiency of phase II trials on average and also ensure that patients are more likely to be treated with the best available treatment for them. Although the designs have drawbacks, the considerable advantages mean that these designs will become increasingly important in phase II. \ua9 2013 Informa Healthcare USA, Inc
Cross-validated risk scores adaptive enrichment (CADEN) design
Full text link\ua9 2024 The AuthorsWe propose a Cross-validated ADaptive ENrichment design (CADEN) in which a trial population is enriched with a subpopulation of patients who are predicted to benefit from the treatment more than an average patient (the sensitive group). This subpopulation is found using a risk score constructed from the baseline (potentially high-dimensional) information about patients. The design incorporates an early stopping rule for futility. Simulation studies are used to assess the properties of CADEN against the original (non-enrichment) cross-validated risk scores (CVRS) design which constructs a risk score at the end of the trial. We show that when there exists a sensitive group of patients, CADEN achieves a higher power and a reduction in the expected sample size compared to the CVRS design. We illustrate the application of the design in two real clinical trials. We conclude that the new design offers improved statistical efficiency over the existing non-enrichment method, as well as increased benefit to patients. The method has been implemented in an R package caden
Developing a predictive signature for two trial endpoints using the cross-validated risk scores method
No full textStage phase II clinical trials with continuous outcomes
No full textTwo-stage designs are commonly used for Phase II trials. Optimal two-stage designs have the lowest expected sample size for a specific treatment effect, for example, the null value, but can perform poorly if the true treatment effect differs. Here we introduce a design for continuous treatment responses that minimizes the maximum expected sample size across all possible treatment effects. The proposed design performs well for a wider range of treatment effects and so is useful for Phase II trials. We compare the design to a previously used optimal design and show it has superior expected sample size properties. Copyright \ua9 Taylor & Francis Group, LLC
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