A Bayesian decision‐theoretic sequential response‐adaptive randomization design

Jiang, Fei; Lee, John; Müller, Peter

doi:10.1002/sim.5735

Cited by 16 publications

(18 citation statements)

References 30 publications

(54 reference statements)

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“…Fortunately, adaptive trial designs, such as responseadaptive randomization and interim stopping rules, allow for modification of the traditional design to improve the efficiency and statistical power in ALS clinical trials [77]. A futility design involves a predetermined threshold for benefit.…”

Section: Learning Phasementioning

confidence: 99%

Clinical Trial Designs in Amyotrophic Lateral Sclerosis: Does One Design Fit All?

2015

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The last 2 decades have seen a surge in the number of amyotrophic lateral sclerosis (ALS) clinical trials with the hope of finding successful treatments. Clinical trialists aim to repurpose existing drugs and test novel compounds to target potential ALS disease pathophysiology. Recent technological advancements have led to the discovery of new causative genetic agents and modes of delivering potential therapy, calling for increasingly sophisticated trial design. The standard ALS clinical trial design may be modified depending on study needs: type of therapy; route of therapy delivery; phase of therapy development; applicable subpopulation; market availability of therapy; and utility of telemedicine. Novel biomarkers of diagnostic, predictive, prognostic, and pharmacodynamic value are undergoing development and validation for use in clinical trials. Design modifications build on the traditional clinical trial design and may be employed in either the learning or confirming trial phase. Novel designs aim to minimize patient risk, study duration, and sample size, while improving efficiency and promoting statistical power to herald an exciting era for clinical research in ALS.

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Section: Learning Phasementioning

confidence: 99%

Clinical Trial Designs in Amyotrophic Lateral Sclerosis: Does One Design Fit All?

2015

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“…We are developing a method for ordinal toxicity and efficacy outcomes using a multinomial logistic regression model, and selecting the dose based on a utility function. To avoid the exploitation versus exploration dilemma, dynamic and non-myopic rules could be explored [26,39,28], which enable to appropriately balance the dilemma between learning about doses' efficacy and allocating more patients to the best dose level. We are pursuing this work with new collaborations; some authors of this paper are exploring the possibility to apply Bandit algorithms [40,41] to standard CRM and then to extend it to unimodal relationship.…”

Section: Resultsmentioning

confidence: 99%

Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization

Riviere

Yuan

Jourdan

et al. 2016

Stat Methods Med Res

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Conventionally, phase I dose-finding trials aim to determine the maximum tolerated dose of a new drug under the assumption that both toxicity and efficacy monotonically increase with the dose. This paradigm, however, is not suitable for some molecularly targeted agents, such as monoclonal antibodies, for which efficacy often increases initially with the dose and then plateaus. For molecularly targeted agents, the goal is to find the optimal dose, defined as the lowest safe dose that achieves the highest efficacy. We develop a Bayesian phase I/II dose-finding design to find the optimal dose. We employ a logistic model with a plateau parameter to capture the increasing-then-plateau feature of the dose-efficacy relationship. We take the weighted likelihood approach to accommodate for the case where efficacy is possibly late-onset. Based on observed data, we continuously update the posterior estimates of toxicity and efficacy probabilities and adaptively assign patients to the optimal dose. The simulation studies show that the proposed design has good operating characteristics. This method is going to be applied in more than two phase I clinical trials as no other method is available for this specific setting. We also provide an R package dfmta that can be downloaded from CRAN website.

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“…Advances in approximation and optimization methods can help alleviate the computational barrier. Also, many authors have provided ad hoc modifications to curtail the extreme allocation property . It is instructive yet complicated to conduct comparisons.…”

Section: Discussionmentioning

confidence: 99%

Frequentist operating characteristics of Bayesian optimal designs via simulation

Zhang

Trippa

Parmigiani

2019

Statistics in Medicine

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Bayesian adaptive designs have become popular because of the possibility of increasing the number of patients treated with more beneficial treatments, while still providing sufficient evidence for treatment efficacy comparisons. It can be essential, for regulatory and other purposes, to conduct frequentist analyses both before and after a Bayesian adaptive trial, and these remain challenging. In this paper, we propose a general simulation-based approach to compare frequentist designs with Bayesian adaptive designs based on frequentist criteria such as power and to compute valid frequentist p-values. We illustrate our approach by comparing the power of an equal randomization (ER) design with that of an optimal Bayesian adaptive (OBA) design. The Bayesian design considered here is the dynamic programming solution of the optimization of a specific utility function defined by the number of successes in a patient horizon, including patients whose treatment will be affected by the trial's results after the end of the trial.While the power of an ER design depends on treatment efficacy and the sample size, the power of the OBA design also depends on the patient horizon size. Our results quantify the trade-off between power and the optimal assignment of patients to treatments within the trial. We show that, for large patient horizons, the two criteria are in agreement, while for small horizons, differences can be substantial. This has implications for precision medicine, where patient horizons are decreasing as a result of increasing stratification of patients into subpopulations defined by molecular markers. KEYWORDSBayesian adaptive designs, dynamic programming, frequentist analyses, optimal strategy Spiegelhalter et al 5 and Cellamare et al 6 have illustrated the use of Bayesian approaches in clinical trials and discussed their advantages. Ethical concerns 7 associated with adaptive designs are also relevant in Bayesian adaptive settings. On the other hand, frequentist criteria remain the mainstay in regulatory decision-making and in the medical literature. This applies to both trial design requirements, such as the power at a given type I error level, and reported 4026

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A Bayesian decision‐theoretic sequential response‐adaptive randomization design

Cited by 16 publications

References 30 publications

Clinical Trial Designs in Amyotrophic Lateral Sclerosis: Does One Design Fit All?

Clinical Trial Designs in Amyotrophic Lateral Sclerosis: Does One Design Fit All?

Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization

Frequentist operating characteristics of Bayesian optimal designs via simulation

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