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Response-Adaptive Randomization (RAR) is recognized for its potential to deliver improvements in patient benefit. However, the utility of RAR is contingent on regularization methods to mitigate early instability and preserve statistical…

Methodology · Statistics 2025-11-27 Lukas Pin , Stef Baas , Gianmarco Caruso , David S. Robertson , Sofía S. Villar

Response-adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates.…

Applications · Statistics 2017-03-14 Sofia S. Villar , Jack Bowden , James Wason

In most clinical trials, patients are randomized with equal probability among treatments to obtain an unbiased estimate of the treatment effect. Response-adaptive randomization (RAR) has been proposed for ethical reasons, where the…

Applications · Statistics 2019-09-16 Thevaa Chandereng , Rick Chappell

The Bayesian Optimal Phase II (BOP2) framework is a flexible trial design that can naturally facilitate complex adaptations due to its Bayesian setting. BOP2 uses equal randomisation and equally placed interim analyses in its design, but it…

Applications · Statistics 2025-11-19 Connor Fitchett , Ayon Mukherjee , Sofía S. Villar , David S. Robertson

Response adaptive randomization (RAR) is appealing from methodological, ethical, and pragmatic perspectives in the sense that subjects are more likely to be randomized to better performing treatment groups based on accumulating data.…

Methodology · Statistics 2022-08-03 Tianyu Zhan , Lu Cui , Ziqian Geng , Lanju Zhang , Yihua Gu , Ivan S. F. Chan

Bayesian adaptive designs enable flexible clinical trials by adapting features based on accumulating data. Among these, Bayesian Response-Adaptive Randomization (BRAR) skews patient allocation towards more promising treatments based on…

Methodology · Statistics 2025-12-05 Daniel Kaddaj , Stef Baas , Edwin Y. N. Tang , David S. Robertson , Lukas Pin , Sofía S. Villar

The majority of response-adaptive randomisation (RAR) designs in the literature rely on efficacy data to guide dynamic patient allocation. However, their applicability becomes limited in settings where efficacy outcomes, such as survival,…

Methodology · Statistics 2026-02-23 Maria Vittoria Chiaruttini , Lukas Pin , Sofia S. Villar

In developing products for rare diseases, statistical challenges arise due to the limited number of patients available for participation in drug trials and other clinical research. Bayesian adaptive clinical trial designs offer the…

Applications · Statistics 2019-09-19 Xiao Wu , Yi Xu , Bradley P. Carlin

In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown particular benefit for more…

Methodology · Statistics 2023-11-27 James Willard , Shirin Golchi , Erica EM Moodie

The win ratio (WR) statistic is increasingly used to evaluate treatment effects based on prioritized composite endpoints, yet existing Bayesian adaptive designs are not directly applicable because the WR is a summary statistic derived from…

Methodology · Statistics 2026-02-20 Di Zhu , Yong Zang

Practical employment of Bayesian trial designs is still rare. Even if accepted in principle, the regulators have commonly required that such designs be calibrated according to an upper bound for the frequentist type I error rate. This…

Methodology · Statistics 2026-03-25 Elja Arjas , Dario Gasbarra

Background. Designing trials to reduce treatment duration is important in several therapeutic areas, including TB and antibiotics. We recently proposed a new randomised trial design to overcome some of the limitations of standard two-arm…

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined…

Methodology · Statistics 2022-06-09 David S. Robertson , Kim May Lee , Boryana C. Lopez-Kolkovska , Sofia S. Villar

Bayesian response adaptive clinical trials are currently evaluating experimental therapies for several diseases. Adaptive decisions, such as pre-planned variations of the randomization probabilities, attempt to accelerate the development of…

Methodology · Statistics 2021-05-25 Marta Bonsaglio , Sandra Fortini , Steffen Ventz , Lorenzo Trippa

Hybrid clinical trials, that borrow real-world data (RWD), are gaining interest, especially for rare diseases. They assume RWD and randomized control arm be exchangeable, but violations can bias results, inflate type I error, or reduce…

Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression model that is commonly used in causal inference and beyond. Its strong predictive performance is supported by well-developed estimation theory,…

Machine Learning · Statistics 2026-02-10 Yan Shuo Tan , Omer Ronen , Theo Saarinen , Bin Yu

Allocating patients to treatment arms during a trial based on the observed responses accumulated prior to the decision point, and sequential adaptation of this allocation,, could minimize the expected number of failures or maximize total…

Methodology · Statistics 2022-09-01 Zhongying Xu , Andriy I. Bandos , Tianzhou Ma , Lu Tang , Victor B. Talisa , Chung-Chou H. Chang

Restricted mean survival time (RMST) is an intuitive summary statistic for time-to-event random variables, and can be used for measuring treatment effects. Compared to hazard ratio, its estimation procedure is robust against the…

Methodology · Statistics 2023-05-25 Ruizhe Chen , Sanjib Basu , Qian Shi

Immunotherapy has transformed cancer treatment, yet its delayed therapeutic effects often lead to non-proportional hazards, rendering many conventional phase II designs underpowered and prone to type I error inflation. To address this…

Methodology · Statistics 2025-09-03 Zhongheng Cai , Haitao Pan

In oncology, phase II or multiple expansion cohort trials are crucial for clinical development plans. This is because they aid in identifying potent agents with sufficient activity to continue development and confirm the proof of concept.…

Methodology · Statistics 2024-05-24 Takuya Yoshimoto , Satoru Shinoda , Kouji Yamamoto , Kouji Tahata
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