Statistical Information Integration and Learning for multiVariate hEalthcaRe data
SILVER Lab
[Our lab is seeking highly motivated master/PhD students and postdoctoral fellows to join our research group! Please do not hesitate to contact the PI if you are interested in any of these researches.]
SILVER lab focuses on developing novel analytical methods in health data science. The developed methods aim to better
Improve clinical trial design and analysis for timely identification of treatment efficacy
Depict patient/hospital-level heterogeneity for treatment effects;
Identify high risk subcohort leading poor health outcomes;
Detect time-varying treatment/exposure effects;
Predict health outcomes promoting monitoring and early intervention.
Our application focuses on (but not limited to) the following populations
Older adults living with high frailty, e.g., AD/ADRD, multimorbidity;
Patients with brain tumor
Patients with heart/cardiovascular diseases/T2D.
The developed methods / methods under development include (but not limited to)
Integrating multivariate secondary/surrogate outcomes into the primary outcome analysis (applications: RCT and RWD);
Integrating summary/partial information from external data to enhance internal data analysis;
AI/ML-assisted analysis of heterogeneity of treatment effects;
ML-assisted modeling for delayed treatment initiation.
Our lab also offers collaboration services and consulting, including (but not limited to)
(Time varying) propensity score matching/weighting for treatment/exposure comparison
Target trial emulation
Real-world evidence/data integration
Time-to-event analysis with time-varying covariates
Prediction of multivariate outcomes using machine learning
Epidemiological design