A novel approach to augment single-arm clinical studies with real-world data

被引:6
|
作者
Sachdeva, Archie [1 ]
Tiwari, Ram C. [2 ]
Guha, Subharup [1 ]
机构
[1] Univ Florida, Dept Biostat, Gainesville, FL 32611 USA
[2] Bristol Myers Squibb, Global Biometr & Data Sci, Summit, NJ USA
基金
美国国家科学基金会;
关键词
Propensity score; covariate balanced inference; real-world data; real-world evidence; randomized clinical trial; CHAINED EQUATIONS; PROPENSITY SCORE; DISTRIBUTIONS; INCORPORATE; IMPUTATION;
D O I
10.1080/10543406.2021.2011902
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
In this paper, we develop a methodology for leveraging real-world data into single-arm clinical trial studies. In recent years, the idea of augmenting randomized clinical trials data with real-world data has emerged as a particularly attractive technique for health organizations and drug developers to accelerate the drug development process. Major regulatory authorities such as the Food and Drug Administration and European Medicines Agency have recognized the potential of utilizing real-world data and are advancing toward making regulatory decisions based on real-world evidence. Several statistical methods have been developed in recent years for borrowing data from real-world sources such as electronic health records, product and disease registries, as well as claims and billing data. We propose a novel approach to augment single-arm clinical trials with the real-world data derived from single or multiple data sources. Furthermore, we illustrate the proposed method in the presence of missing data and conduct simulation studies to evaluate its performance in diverse settings.
引用
收藏
页码:141 / 157
页数:17
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