scholarly journals The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Trials ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Munyaradzi Dimairo ◽  
◽  
Philip Pallmann ◽  
James Wason ◽  
Susan Todd ◽  
...  
Keyword(s):  
Adaptive Design ◽  
Adaptive Designs ◽  
Randomised Trials

scholarly journals The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

BMJ ◽  
2020 ◽  
pp. m115 ◽  
Author(s):  
Munyaradzi Dimairo ◽  
Philip Pallmann ◽  
James Wason ◽  
Susan Todd ◽  
Thomas Jaki ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Adaptive Design ◽  
Randomised Trial ◽  
Adaptive Designs ◽  
Future Research ◽  
Randomised Trials ◽  
Public And Private ◽  
Key Stakeholders ◽  
Potential Benefits ◽  
Explanatory Text

AbstractAdaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.

scholarly journals The Adaptive designs CONSORT Extension (ACE) Statement: A checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

2019 ◽  
Author(s):  
Munyaradzi Dimairo ◽  
Philip Pallmann ◽  
James Wason ◽  
Susan Todd ◽  
Thomas Jaki ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Adaptive Design ◽  
Randomised Trial ◽  
Adaptive Designs ◽  
Future Research ◽  
Randomised Trials ◽  
Public And Private ◽  
Key Stakeholders ◽  
Potential Benefits ◽  
Explanatory Text

Abstract Background Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised. Methods This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 Statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process. Results The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist is comprised of seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text. Conclusions The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.

DIA’s Adaptive Design Scientific Working Group (ADSWG): Best Practices Case Studies for “Less Well-understood” Adaptive Designs

2017 ◽  
Vol 51 (1) ◽  
pp. 77-88 ◽  
Author(s):  
Eva Miller ◽  
Paul Gallo ◽  
Weili He ◽  
Lisa A. Kammerman ◽  
Kenneth Koury ◽  
...  
Keyword(s):  
Best Practices ◽  
Case Studies ◽  
Working Group ◽  
Adaptive Design ◽  
Adaptive Designs

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

2017 ◽  
Vol 14 (5) ◽  
pp. 417-424 ◽  
Author(s):  
Frank Bretz ◽  
Paul Gallo ◽  
Willi Maurer
Keyword(s):  
Clinical Trial ◽  
Adaptive Design ◽  
Clinical Trial Data ◽  
Parameter Estimate ◽  
Information Value ◽  
Adaptive Designs ◽  
Success Rates ◽  
Linear Regression Models ◽  
Regulatory Guidance ◽  
Trace Back

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.

scholarly journals Development process of a consensus-driven CONSORT extension for randomised trials using an adaptive design

BMC Medicine ◽  
2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Munyaradzi Dimairo ◽  
Elizabeth Coates ◽  
Philip Pallmann ◽  
Susan Todd ◽  
Steven A. Julious ◽  
...  
Keyword(s):  
Development Process ◽  
Adaptive Design ◽  
Randomised Trials

A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes

2016 ◽  
Vol 55 (01) ◽  
pp. 4-13
Author(s):  
M. Moatti ◽  
S. Zohar ◽  
W. F. Rosenberger ◽  
S. Chevret
Keyword(s):  
Clinical Trial ◽  
Multiple Myeloma ◽  
Clinical Trials ◽  
Adaptive Design ◽  
Hazard Ratio ◽  
Adaptive Designs ◽  
Stopping Rules ◽  
Phase Iii ◽  
Optimal Criterion ◽  
Fully Bayesian

SummaryBackground: Response-adaptive randomisation designs have been proposed to im -prove the efficiency of phase III randomised clinical trials and improve the outcomes of the clinical trial population. In the setting of failure time outcomes, Zhang and Rosen -berger (2007) developed a response-adaptive randomisation approach that targets an optimal allocation, based on a fixed sample size. Objectives: The aim of this research is to propose a response-adaptive randomisation procedure for survival trials with an interim monitoring plan, based on the following optimal criterion: for fixed variance of the esti -mated log hazard ratio, what allocation minimizes the expected hazard of failure? We demonstrate the utility of the design by re -designing a clinical trial on multiple myeloma. Methods: To handle continuous monitoring of data, we propose a Bayesian response-adap -tive randomisation procedure, where the log hazard ratio is the effect measure of interest. Combining the prior with the normal likelihood, the mean posterior estimate of the log hazard ratio allows derivation of the optimal target allocation. We perform a simu lationstudy to assess and compare the perform -ance of this proposed Bayesian hybrid adaptive design to those of fixed, sequential or adaptive – either frequentist or fully Bayesian – designs. Non informative normal priors of the log hazard ratio were used, as well as mixture of enthusiastic and skeptical priors. Stopping rules based on the posterior dis -tribution of the log hazard ratio were com -puted. The method is then illus trated by redesigning a phase III randomised clinical trial of chemotherapy in patients with multiple myeloma, with mixture of normal priors elicited from experts. Results: As expected, there was a reduction in the proportion of observed deaths in the adaptive vs. non-adaptive designs; this reduction was maximized using a Bayes mix -ture prior, with no clear-cut improvement by using a fully Bayesian procedure. The use of stopping rules allows a slight decrease in the observed proportion of deaths under the alternate hypothesis compared with the adaptive designs with no stopping rules. Conclusions: Such Bayesian hybrid adaptive survival trials may be promising alternatives to traditional designs, reducing the duration of survival trials, as well as optimizing the ethical concerns for patients enrolled in the trial.

Improving an Ergonomics Testing Procedure via Approximation-based Adaptive Experimental Design

2004 ◽  
Vol 127 (5) ◽  
pp. 1006-1013 ◽  
Author(s):  
Michael J. Sasena ◽  
Matthew Parkinson ◽  
Matthew P. Reed ◽  
Panos Y. Papalambros ◽  
Pierre Goovaerts
Keyword(s):  
Experimental Design ◽  
Adaptive Design ◽  
Testing Procedure ◽  
Test Subject ◽  
Adaptive Designs ◽  
Sample Point ◽  
Sampling Points ◽  
The Individual ◽  
Adaptive Experimental Design ◽  
Selection Of

Adaptive design refers to experimental design where the next sample point is determined by information from previous experiments. This article presents a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch, and Jones) that can create adaptive designs using kriging approximations. Our primary goal is to illustrate that superEGO is well-suited to generating adaptive designs which have many advantages over competing methods. The approach is demonstrated on a novel human-reach experiment where the selection of sampling points adapts to the individual test subject. Results indicate that superEGO is effective at satisfying the experimental objectives.

scholarly journals Adaptive design clinical trials: a review of the literature and ClinicalTrials.gov

BMJ Open ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. e018320 ◽  
Author(s):  
Laura E Bothwell ◽  
Jerry Avorn ◽  
Nazleen F Khan ◽  
Aaron S Kesselheim
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Interim Analysis ◽  
Adaptive Design ◽  
Drug Application ◽  
Adaptive Designs ◽  
European Medicines Agency ◽  
Product Approval ◽  
New Drug ◽  
Adaptive Trials

ObjectivesThis review investigates characteristics of implemented adaptive design clinical trials and provides examples of regulatory experience with such trials.DesignReview of adaptive design clinical trials in EMBASE, PubMed, Cochrane Registry of Controlled Clinical Trials, Web of Science and ClinicalTrials.gov. Phase I and seamless Phase I/II trials were excluded. Variables extracted from trials included basic study characteristics, adaptive design features, size and use of independent data monitoring committees (DMCs) and blinded interim analyses. We also examined use of the adaptive trials in new drug submissions to the Food and Drug Administration (FDA) and European Medicines Agency (EMA) and recorded regulators’ experiences with adaptive designs.Results142 studies met inclusion criteria. There has been a recent growth in publicly reported use of adaptive designs among researchers around the world. The most frequently appearing types of adaptations were seamless Phase II/III (57%), group sequential (21%), biomarker adaptive (20%), and adaptive dose-finding designs (16%). About one-third (32%) of trials reported an independent DMC, while 6% reported blinded interim analysis. We found that 9% of adaptive trials were used for FDA product approval consideration, and 12% were used for EMA product approval consideration. International regulators had mixed experiences with adaptive trials. Many product applications with adaptive trials had extensive correspondence between drug sponsors and regulators regarding the adaptive designs, in some cases with regulators requiring revisions or alterations to research designs.ConclusionsWider use of adaptive designs will necessitate new drug application sponsors to engage with regulatory scientists during planning and conduct of the trials. Investigators need to more consistently report protections intended to preserve confidentiality and minimise potential operational bias during interim analysis.

scholarly journals Adaptive Design for Staggered-Start Clinical Trial

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Ao Yuan ◽  
Qizhai Li ◽  
Ming Xiong ◽  
Ming T. Tan
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Adaptive Design ◽  
Optimality Criterion ◽  
Total Sample ◽  
Adaptive Designs ◽  
Initial Sample ◽  
Total Sample Size ◽  
New Type ◽  
Start Trial

AbstractIn phase II and/or III clinical trial study, there are several competing treatments, the goal is to assess the performances of the treatments at the end of the study, the trial design aims to minimize risks to the patients in the trial, according to some given allocation optimality criterion. Recently, a new type of clinical trial, the staggered-start trial has been proposed in some studies, in which different treatments enter the same trial at different times. Some basic questions for this trial are whether optimality can still be kept? under what conditions? and if so how to allocate the the coming patients to treatments to achieve such optimality? Here we propose and study a class of adaptive designs of staggered-start clinical trials, in which for given optimality criterion object, we show that as long as the initial sizes at the beginning of the successive trials are not too large relative to the total sample size, the proposed design can still achieve optimality criterion asymptotically for the allocation proportions as the ordinary trials; if these initial sample sizes have about the same magnitude as the total sample size, full optimality cannot be achieved. The proposed method is simple to use and is illustrated with several examples and a simulation study.

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