贝叶斯概率
协变量
临时的
中期分析
临床试验
计算机科学
随机对照试验
计量经济学
数据挖掘
医学
机器学习
人工智能
数学
内科学
历史
考古
作者
Cheng Huang,Chenghao Chu,Yun Lu,Bingming Yi,Ming‐Hui Chen
出处
期刊:The New England Journal of Statistics in Data Science
[New England Statistical Society]
日期:2023-01-01
卷期号:: 1-18
摘要
Basket trials have captured much attention in oncology research in recent years, as advances in health technology have opened up the possibility of classification of patients at the genomic level. Bayesian methods are particularly prevalent in basket trials as the hierarchical structure is adapted to basket trials to allow for information borrowing. In this article, we extend the Bayesian methods to basket trials with treatment and control arms for continuous endpoints, which are often the cases in clinical trials for rare diseases. To account for the imbalance in the covariates which are potentially strong predictors but not stratified in a randomized trial, our models make adjustments for these covariates, and allow different coefficients across baskets. In addition, comparisons are drawn between two-stage design and one-stage design for the four Bayesian methods. Extensive simulation studies are conducted to examine the empirical performance of all models under consideration. A real data analysis is carried out to further demonstrate the usefulness of the Bayesian methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI