ArticleStatistical methods in medical research2018
Extended two-stage adaptive designs with three target responses for phase II clinical trials.
Article in Statistical methods in medical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Nature-inspired metaheuristics for optimizing dose-finding and computationally challenging clinical trial designs.Clinical trials (London, England) · 2025Article
- Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.Diabetologia · 2025Review
- An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications.Journal of medical Internet research · 2023Article
- Spatial Two-stage Designs for Phase II Clinical Trials.Computational statistics & data analysis · 2022Article
- Stopping for efficacy in single-arm phase II clinical trials.Journal of applied statistics · 2022Article
- Metaheuristics for pharmacometrics.CPT: pharmacometrics & systems pharmacology · 2021Review
- Article
- Article
- Phase II Two-Stage Single-Arm Clinical Trials for Testing Toxicity Levels.Communications for statistical applications and methods · 2019Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial. Our proposed designs include the celebrated Simon's two-stage design and its extension that allows two target response rates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperforms greedy algorithms, which are currently used to find such designs when there are only a few parameters; it is also capable of solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of our proposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one of the three target response rates of the drug, subject to various user-specified testing error rates. Our designs are therefore more flexible and interestingly, do not necessarily require larger expected sample size requirements than two-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires one half fewer subjects than the implemented design in the study.
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