Leveraging machine learning: Covariate-adjusted Bayesian adaptive randomization and subgroup discovery in multi-arm survival trials

IF 2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Wenxuan Xiong , Jason Roy , Hao Liu , Liangyuan Hu
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引用次数: 0

Abstract

Clinical trials evaluate the safety and efficacy of treatments for specific diseases. Ensuring these studies are well-powered is crucial for identifying superior treatments. With the rise of personalized medicine, treatment efficacy may vary based on biomarker profiles. However, researchers often lack prior knowledge about which biomarkers are linked to varied treatment effects. Fixed or response-adaptive designs may not sufficiently account for heterogeneous patient characteristics, such as genetic diversity, potentially reducing the chance of selecting the optimal treatment for individuals. Recent advances in Bayesian nonparametric modeling pave the way for innovative trial designs that not only maintain robust power but also offer the flexibility to identify subgroups deriving greater benefits from specific treatments. Building on this inspiration, we introduce a Bayesian adaptive design for multi-arm trials focusing on time-to-event endpoints. We introduce a covariate-adjusted response adaptive randomization, updating treatment allocation probabilities grounded on causal effect estimates using a random intercept accelerated failure time BART model. After the trial concludes, we suggest employing a multi-response decision tree to pinpoint subgroups with varying treatment impacts. The performance of our design is then assessed via comprehensive simulations.

利用机器学习:多臂生存试验中的协变量调整贝叶斯自适应随机化和亚组发现
临床试验评估特定疾病治疗方法的安全性和有效性。确保这些研究具有良好的动力,对于确定卓越的治疗方法至关重要。随着个性化医疗的兴起,治疗效果可能会根据生物标志物的特征而有所不同。然而,研究人员往往事先不知道哪些生物标志物与不同的治疗效果有关。固定或反应适应性设计可能无法充分考虑患者的异质性特征,如遗传多样性,从而可能降低为个体选择最佳治疗方法的机会。贝叶斯非参数建模的最新进展为创新性试验设计铺平了道路,这种设计不仅能保持稳健的功率,还能灵活识别从特定治疗中获益更多的亚组。在此启发下,我们为多臂试验引入了贝叶斯自适应设计,重点关注时间到事件终点。我们引入了一种协变量调整反应自适应随机化,使用随机截距加速失败时间 BART 模型,根据因果效应估计值更新治疗分配概率。试验结束后,我们建议采用多反应决策树来确定治疗效果不同的亚组。然后通过综合模拟来评估我们设计的性能。
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来源期刊
CiteScore
3.70
自引率
4.50%
发文量
281
审稿时长
44 days
期刊介绍: Contemporary Clinical Trials is an international peer reviewed journal that publishes manuscripts pertaining to all aspects of clinical trials, including, but not limited to, design, conduct, analysis, regulation and ethics. Manuscripts submitted should appeal to a readership drawn from disciplines including medicine, biostatistics, epidemiology, computer science, management science, behavioural science, pharmaceutical science, and bioethics. Full-length papers and short communications not exceeding 1,500 words, as well as systemic reviews of clinical trials and methodologies will be published. Perspectives/commentaries on current issues and the impact of clinical trials on the practice of medicine and health policy are also welcome.
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