scholarly journals The Bayesian Design of Adaptive Clinical Trials

2021 ◽  
Vol 18 (2) ◽  
pp. 530
Author(s):  
Alessandra Giovagnoli
Keyword(s):  
Clinical Trials ◽  
Adaptive Design ◽  
Recent Literature ◽  
Adaptive Designs ◽  
Bayesian Design ◽  
High Expectations ◽  
Reading Material ◽  
Adaptive Trials ◽  
Adaptive Clinical Trials ◽  
Interesting Reading

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.

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 Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Nina Wilson ◽  
Katie Biggs ◽  
Sarah Bowden ◽  
Julia Brown ◽  
Munyaradzi Dimairo ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Management ◽  
Adaptive Design ◽  
Adaptive Designs ◽  
Great Promise ◽  
Percentage Increase ◽  
Trial Management ◽  
High Quality ◽  
Adaptive Trials ◽  
Adaptive Trial

Abstract Background Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials. Methods We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences. Results Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had. Conclusions This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive 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.

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.

scholarly journals Adapt to Translate

2021 ◽  
Vol 17 (2) ◽  
pp. 5-24
Author(s):  
Daria Jadreškić
Keyword(s):  
Clinical Trials ◽  
Clinical Practice ◽  
Clinical Research ◽  
Pharmaceutical Research ◽  
Lessons Learned ◽  
Operational Conditions ◽  
Medical Interventions ◽  
Adaptive Trials ◽  
Adaptive Clinical Trials ◽  
Normative Argument

The article presents the advantages and limitations of adaptive clinical trials for assessing the effectiveness of medical interventions and specifies the conditions that contributed to their development and implementation in clinical practice. I advance two arguments by discussing different cases of adaptive trials. The normative argument is that responsible adaptation should be taken seriously as a new way of doing clinical research insofar as a valid justification, sufficient understanding, and adequate operational conditions are provided. The second argument is historical. The development of adaptive trials can be related to lessons learned from research in cases of urgency and to the decades-long efforts to end the productivity crisis of pharmaceutical research, which led to the emergence of translational, personalized, and, recently, precision medicine movements.

Adaptive designs from a Data Safety Monitoring Board perspective: Some controversies and some case studies

2017 ◽  
Vol 14 (5) ◽  
pp. 462-469 ◽  
Author(s):  
Bruce W Turnbull
Keyword(s):  
Clinical Trials ◽  
Case Studies ◽  
Adaptive Design ◽  
Safety Monitoring ◽  
Data Safety Monitoring Board ◽  
Adaptive Designs ◽  
Data Safety ◽  
Safety Monitoring Board ◽  
Data Safety Monitoring ◽  
Personal Experiences

This article describes vignettes concerning interactions with Data Safety Monitoring Boards during the design and monitoring of some clinical trials with an adaptive design. Most reflect personal experiences by the author.

scholarly journals A new inferential approach for response-adaptive clinical trials: the variance-stabilized bootstrap

Test ◽  
2021 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Marco Novelli ◽  
Maroussa Zagoraiou
Keyword(s):  
Clinical Trials ◽  
Adaptive Designs ◽  
Simulation Studies ◽  
Adaptive Randomization ◽  
Allocation Procedure ◽  
Adaptive Clinical Trials ◽  
Response Adaptive Designs ◽  
Treatment Comparisons

AbstractThis paper discusses disadvantages and limitations of the available inferential approaches in sequential clinical trials for treatment comparisons managed via response-adaptive randomization. Then, we propose an inferential methodology for response-adaptive designs which, by exploiting a variance stabilizing transformation into a bootstrap framework, is able to overcome the above-mentioned drawbacks, regardless of the chosen allocation procedure as well as the desired target. We derive the theoretical properties of the suggested proposal, showing its superiority with respect to likelihood, randomization and design-based inferential approaches. Several illustrative examples and simulation studies are provided in order to confirm the relevance of our results.

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.

Bayesian optimal response-adaptive design for binary responses using stopping rule

2016 ◽  
Vol 27 (3) ◽  
pp. 891-904 ◽  
Author(s):  
Fumiyasu Komaki ◽  
Atanu Biswas
Keyword(s):  
Clinical Trials ◽  
Adaptive Design ◽  
Real Data ◽  
Adaptive Designs ◽  
Phase Iii ◽  
Data Set ◽  
Binary Responses ◽  
Phase Iii Clinical Trials ◽  
Number Of Patients ◽  
Response Adaptive Designs

Response-adaptive designs are used in phase III clinical trials to allocate a larger number of patients to the better treatment arm. Optimal designs are explored in the recent years in the context of response-adaptive designs, in the frequentist view point only. In the present paper, we propose some response-adaptive designs for two treatments based on Bayesian prediction for phase III clinical trials. Some properties are studied and numerically compared with some existing competitors. A real data set is used to illustrate the applicability of the proposed methodology where we redesign the experiment using parameters derived from the data set.

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