频数推理
生物标志物
临床试验
临床研究设计
计算机科学
贝叶斯概率
精密医学
医学
研究设计
医学物理学
数据科学
管理科学
风险分析(工程)
人工智能
贝叶斯推理
生物
内科学
病理
工程类
数学
生物化学
统计
作者
Yue Tu,Lindsay A. Renfro
标识
DOI:10.1080/10543406.2024.2358806
摘要
Due to increased use of gene sequencing techniques, understanding of cancer on a molecular level has evolved, in terms of both diagnosis and evaluation in response to initial therapies. In parallel, clinical trials meant to evaluate molecularly-driven interventions through assessment of both treatment effects and putative predictive biomarker effects are being employed to advance the goals of precision medicine. Basket trials investigate one or more biomarker-targeted therapies across multiple cancer types in a tumor location agnostic fashion. The review article offers an overview of the traditional forms of such designs, the practical challenges facing each type of design, and then review novel adaptations proposed in the last few years, categorized into Bayesian and Classical Frequentist perspectives. The review article concludes by summarizing potential advantages and limitations of the new trial design solutions.
科研通智能强力驱动
Strongly Powered by AbleSci AI