Bayesian selective response‐adaptive design using the historical control

Kim, Miok; Harun, Nusrat; Liu, Chunyan; Broderick, Joseph P.

doi:10.1002/sim.7836

Cited by 7 publications

(19 citation statements)

References 31 publications

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“…In this paper, we fairly evaluate borrowing and no-borrowing designs by taking this into consideration. The recently proposed selective response-adaptive randomization design and stroke trial settings of Kim et al 3 were used in the simulation studies. The overall results, however, are not specific to the design or the clinical setting in use.…”

Section: Discussionmentioning

confidence: 99%

“…Any violation to the assumed commensurability will negatively impact the operating characteristics of both borrowing and no borrowing. We will investigate the impact using a stroke trial example and selective adaptive randomization design proposed in our earlier work …”

Section: Motivation and Methodsmentioning

confidence: 99%

“…We describe the computation of effective historical sample size and allocation probabilities in this subsection. We refer to Kim et al 3 for more details including the specification of the likelihood and the priors.…”

Section: Bayesian Dynamic Borrowing With Selective Response-adaptivmentioning

confidence: 99%

“…Efficient use of the existing information can lead to shorter trials, reduce both trial cost and sample size, and may avoid exposure of patients to the inferior treatment . A recently proposed design used both historical borrowing and selective adaptive randomization and assigned fewer current trial subjects to the control arm only if the investigational arm appeared superior. When applied to well‐publicized stroke trials, simulation studies showed that on average 63% or 25% less subjects were enrolled with anticipated reduction in trial duration by 637 days with 69.2% subjects receiving better treatment in patients with severe stroke under the alternative setting.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we account for the practical requirement of Bayesian decision rule calibration and compare borrowing with no borrowing under various imperfectly commensurate scenarios. The design and clinical context of Kim et al 3 have been used.…”

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confidence: 99%

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Critical appraisal of Bayesian dynamic borrowing from an imperfectly commensurate historical control

Harun

Liu

Kim

2020

Pharmaceutical Statistics

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Summary Bayesian dynamic borrowing designs facilitate borrowing information from historical studies. Historical data, when perfectly commensurate with current data, have been shown to reduce the trial duration and the sample size, while inflation in the type I error and reduction in the power have been reported, when imperfectly commensurate. These results, however, were obtained without considering that Bayesian designs are calibrated to meet regulatory requirements in practice and even no‐borrowing designs may use information from historical data in the calibration. The implicit borrowing of historical data suggests that imperfectly commensurate historical data may similarly impact no‐borrowing designs negatively. We will provide a fair appraiser of Bayesian dynamic borrowing and no‐borrowing designs. We used a published selective adaptive randomization design and real clinical trial setting and conducted simulation studies under varying degrees of imperfectly commensurate historical control scenarios. The type I error was inflated under the null scenario of no intervention effect, while larger inflation was noted with borrowing. The larger inflation in type I error under the null setting can be offset by the greater probability to stop early correctly under the alternative. Response rates were estimated more precisely and the average sample size was smaller with borrowing. The expected increase in bias with borrowing was noted, but was negligible. Using Bayesian dynamic borrowing designs may improve trial efficiency by stopping trials early correctly and reducing trial length at the small cost of inflated type I error.

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Section: Discussionmentioning

confidence: 99%

Section: Motivation and Methodsmentioning

confidence: 99%

Section: Bayesian Dynamic Borrowing With Selective Response-adaptivmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Critical appraisal of Bayesian dynamic borrowing from an imperfectly commensurate historical control

Harun

Liu

Kim

2020

Pharmaceutical Statistics

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Bayesian adaptive design for covariate‐adaptive historical control information borrowing

Jin,

Kim,

Scheffler

et al. 2023

Statistics in Medicine

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Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study‐level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate‐adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate‐dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo‐controlled randomized trials on vertebral fracture risk in post‐menopausal women.

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