4 papers
Doubly Robust Fusion of Many Treatments for Policy Learning
Ke Zhu, Jianing Chu, Ilya Lipkovich +2
Individualized treatment rules/recommendations (ITRs) aim to improve patient outcomes by tailoring treatments to the characteristics of each individual. However, when there are man…
Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials
Chenyin Gao, Shu Yang, Mingyang Shan +3
Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Re…
Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study
Xiaoqing Tan, Shu Yang, Wenyu Ye +3
Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have b…
Improving randomized controlled trial analysis via data-adaptive borrowing
Chenyin Gao, Shu Yang, Mingyang Shan +3
In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balan…