Seamless Phase Ii/Iii Clinical Trials With Covariate Adaptive Randomization

Ma, Wei; Wang, Mengxi; Zhu, Hongjian

doi:10.5705/ss.202019.0354

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…Ma et al [ 14 ] studied seamless phase 2/3 clinical trials with covariate-adaptive randomization and provided a theoretical foundation for the complex procedure. On the basis of their work, a simple modified version of the t test can be used, and the power of detecting the treatment effect can be increased by approximately 10% compared with the traditional approach.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications

Zhu¹,

Wong²

2023

J Med Internet Res

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Adaptive designs are increasingly developed and used to improve all phases of clinical trials and in biomedical studies in various ways to address different statistical issues. We first present an overview of adaptive designs and note their numerous advantages over traditional clinical trials. In particular, we provide a concrete demonstration that shows how recent adaptive design strategies can further improve an adaptive trial implemented 13 years ago. Despite their usefulness, adaptive designs are still not widely implemented in clinical trials. We offer a few possible reasons and propose some ways to use them more broadly in practice, which include greater availability of software tools and interactive websites to generate optimal adaptive trials freely and effectively, including the use of metaheuristics to facilitate the search for an efficient trial design. To this end, we present several web-based tools for finding various adaptive and nonadaptive optimal designs and discuss nature-inspired metaheuristics. Metaheuristics are assumptions-free general purpose optimization algorithms widely used in computer science and engineering to tackle all kinds of challenging optimization problems, and their use in designing clinical trials is just emerging. We describe a few recent such applications and some of their capabilities for designing various complex trials. Particle swarm optimization is an exemplary nature-inspired algorithm, and similar to others, it has a simple definition but many moving parts, making it hard to study its properties analytically. We investigated one of its hitherto unstudied issues on how to bring back out-of-range candidates during the search for the optimum of the search domain and show that different strategies can impact the success and time of the search. We conclude with a few caveats on the use of metaheuristics for a successful search.

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Section: Introductionmentioning

confidence: 99%

An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications

Zhu¹,

Wong²

2023

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications (Preprint)

Zhu,

Wong

2022

Preprint

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UNSTRUCTURED Adaptive designs are increasingly developed and used to improve all phases of clinical trials and in biomedical studies in various ways to address different statistical issues. We first present an overview of adaptive designs and note their numerous advantages over traditional clinical trials. In particular, we provide a concrete demonstration that shows how recent adaptive design strategies can further improve an adaptive trial implemented 13 years ago. Despite their usefulness, adaptive designs are still not widely implemented in clinical trials. We offer a few possible reasons and propose some ways to use them more broadly in practice, which include greater availability of software tools and interactive websites to generate optimal adaptive trials freely and effectively, including the use of metaheuristics to facilitate the search for an efficient trial design. To this end, we present several web-based tools for finding various adaptive and nonadaptive optimal designs and discuss nature-inspired metaheuristics. Metaheuristics are assumptions-free general purpose optimization algorithms widely used in computer science and engineering to tackle all kinds of challenging optimization problems, and their use in designing clinical trials is just emerging. We describe a few recent such applications and some of their capabilities for designing various complex trials. Particle swarm optimization is an exemplary nature-inspired algorithm, and similar to others, it has a simple definition but many moving parts, making it hard to study its properties analytically. We investigated one of its hitherto unstudied issues on how to bring back out-of-range candidates during the search for the optimum of the search domain and show that different strategies can impact the success and time of the search. We conclude with a few caveats on the use of metaheuristics for a successful search.

show abstract

Seamless Phase Ii/Iii Clinical Trials With Covariate Adaptive Randomization

Cited by 2 publications

References 47 publications

An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications

An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications

An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications (Preprint)

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