5 papers
Trustworthy AI/ML Regression and Unbiased Causal Inference for Real-World Data
Yifei Xu, Hwiyoung Lee, Zhenyao Ye +5
Real-World Data (RWD), with its large sample sizes and rich clinical detail, offers a compelling alternative to randomized controlled trials (RCTs) for studying treatment effects i…
Estimate Time-Varying Exposure Effects via Ensemble Learning-based Marginal Structural Model with Application to Adolescent Cognitive Development Study
Zhiwei Zhao, Chixiang Chen, Shuo Chen
Evaluating the effects of time-varying exposures is essential for longitudinal studies. The effect estimation becomes increasingly challenging when dealing with hundreds of time-de…
A New Integrative Learning Framework for Integrating Multiple Secondary Outcomes into Primary Outcome Analysis: A Case Study on Liver Health
Daxuan Deng, Peisong Han, Shuo Chen +2
In the era of big data, secondary outcomes have become increasingly important alongside primary outcomes. These secondary outcomes, which can be derived from traditional endpoints…
Modeling Interconnected Modules in Multivariate Outcomes: Evaluating the Impact of Alcohol Intake on Plasma Metabolomics
Yifan Yang, Chixiang Chen, Hwiyoung Lee +2
Alcohol consumption has been shown to influence cardiovascular mechanisms in humans, leading to observable alterations in the plasma metabolomic profile. Regression models are comm…
Multiple Imputation Method for High-Dimensional Neuroimaging Data
Tong Lu, Chixiang Chen, Hsin-Hsiung Huang +3
Missingness is a common issue for neuroimaging data, and neglecting it in downstream statistical analysis can introduce bias and lead to misguided inferential conclusions. It is th…