The Bayesian Design of Adaptive Clinical Trials

Giovagnoli, Alessandra

doi:10.3390/ijerph18020530

Cited by 29 publications

(16 citation statements)

References 111 publications

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“…For instance, the Bonferroni-Holm test procedure based on conditional error rates of individual treatment-control comparisons. 50 Although the class of adaptive designs is broad to the extent that the methodological and statistical challenges (and solutions) are individually determined, 51 critics often overlook this and paint such challenges with a broad brush. 52 For instance, within the statistical community, there is a schism about the benefits of using either the frequentist or Bayesian approach in ADs that use response-adaptive randomisation, 53 some of which are tied to the reliability of the p values of the evaluated parameters.…”

Section: Original Researchmentioning

confidence: 99%

Non-static framework for understanding adaptive designs: an ethical justification in paediatric trials

Afolabi

Kelly

2021

J Med Ethics

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Many drugs used in paediatric medicine are off-label. There is a rising call for the use of adaptive clinical trial designs (ADs) in responding to the need for safe and effective drugs given their potential to offer efficiency and cost-effective benefits compared with traditional clinical trials. ADs have a strong appeal in paediatric clinical trials given the small number of available participants, limited understanding of age-related variability and the desire to limit exposure to futile or unsafe interventions. Although the ethical value of adaptive trials has increasingly come under scrutiny, there is a paucity of literature on the ethical dilemmas that may be associated with paediatric adaptive designs (PADs). This paper highlights some of these ethical concerns around safety, scientific/social value and caregiver/guardian comprehension of the trial design. Against this background, the paper develops a non-static conceptual lens for understanding PADs. It shows that ADs are epistemically open and reduce some of the knowledge-associated uncertainties inherent in clinical trials as well as fast-track the time to draw conclusions about the value of evaluated drugs/treatments. On this note, the authors argue that PADs are ethically justifiable given they (1) have multiple layers of safety, exposing enrolled children to lesser potential risks, (2) create social/scientific value generally and for paediatric populations in particular, (3) specifically foster the flourishing of paediatric populations and (4) can significantly improve paediatric trial efficiency when properly designed and implemented. However, because PADs are relatively new and their regulatory, ethical and logistical characteristics are yet to be clarified in some jurisdictions, the cooperation of various public and private stakeholders is required to ensure that the interests of children, their caregivers and parents/guardians are best served while exposing paediatric research subjects to the most minimal of risks when they are enrolled in paediatric trials that use ADs.

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Section: Original Researchmentioning

confidence: 99%

Non-static framework for understanding adaptive designs: an ethical justification in paediatric trials

Afolabi

Kelly

2021

J Med Ethics

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“…We suspect that the outlined Bayesian approaches remain under-utilized because researchers are relatively unfamiliar with them and because easily accessible software implementations are currently not available [ 34 , 68 ]. Moreover, there is a noteworthy lack of official FDA and EMA guidelines for Bayesian analyses [ 69 ]. The goal of this paper is therefore three-fold.…”

Section: Introductionmentioning

confidence: 99%

Informed Bayesian survival analysis

Bartoš

Aust

Haaf

2022

BMC Med Res Methodol

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Background We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process. Methods We illustrate the application of the outlined Bayesian approaches on an example data set, retrospective re-analyzing a colon cancer trial. We assess the performance of Bayesian parametric survival analysis and maximum likelihood survival models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study. Results In the retrospective re-analysis of the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect of adding Cetuximab to FOLFOX6 regimen on disease-free survival in patients with resected stage III colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We found no noticeable differences for survival predictions. We have made the analytic approach readily available to other researchers in the RoBSA R package. Conclusions The outlined Bayesian framework provides several benefits when applied to parametric survival analyses. It uses data more efficiently, is capable of considerably shortening the length of clinical trials, and provides a richer set of inferences.

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“…In a clinical trial, it is even possible to continuously monitor the outcome data as they are observed, and thereby utilize such data in a fully adaptive fashion during the execution of the trial. The advantages of this approach are summarized neatly in the short review paper Berry (2006), in , Lee and Chu (2012), and more recently, in Yin et al (2017), Ruberg et al (2019) and Giovagnoli (2021). The paper Villar et al (2015) contains a useful review of the theoretical background, connecting the theory of the optimal design of clinical trials with that of multi-armed bandit problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Arjas

Gasbarra

2021

Preprint

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Background: Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power. Results: The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during Phase II and III. This approach is based on comparing the performance of the different treatment arm in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm (Rule 1), and treatment selection, removing an arm from the trial permanently (Rule 2). The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package. Conclusion: The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

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The Bayesian Design of Adaptive Clinical Trials

Cited by 29 publications

References 111 publications

Non-static framework for understanding adaptive designs: an ethical justification in paediatric trials

Non-static framework for understanding adaptive designs: an ethical justification in paediatric trials

Informed Bayesian survival analysis

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

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