3 papers
stat.ME2025
Covariate Adjustment Cannot Hurt: Treatment Effect Estimation under Interference with Low-Order Outcome Interactions
Xinyi Wang, Shuangning Li
In randomized experiments, covariates are often used to reduce variance and improve the precision of treatment effect estimates. However, in many real-world settings, interference…
stat.ME2024
Optimizing Sample Size for Supervised Machine Learning with Bulk Transcriptomic Sequencing: A Learning Curve Approach
Yunhui Qi, Xinyi Wang, Li-Xuan Qin
Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that e…
stat.ME2024
A Semiparametric Approach for Robust and Efficient Learning with Biobank Data
Molei Liu, Xinyi Wang, Chuan Hong
With the increasing availability of electronic health records (EHR) linked with biobank data for translational research, a critical step in realizing its potential is to accurately…