14 citations · 18 across the 9 of their papers we have counts for
9 papers
Group LASSO Variable Selection Method for Treatment Effect Generalization
Chuyu Deng, Brandon Koch, David M. Vock +1
Often in public health, we are interested in the treatment effect of an intervention on a population that is systemically different from the experimental population the interventio…
Estimating Longitudinal Causal Effects with Unobserved Noncompliance Using a Semi-Parametric G-computation Algorithm
Ross L Peterson, David M Vock, Joseph S Koopmeiners
Participant noncompliance, in which participants do not follow their assigned treatment protocol, often obscures the causal relationship between treatment and treatment effect in r…
Practical Guidance on Modeling Choices for the Virtual Twins Method
Chuyu Deng, David M. Vock, Dana M. Carroll +4
Individuals can vary drastically in their response to the same treatment, and this heterogeneity has driven the push for more personalized medicine. Accurate and interpretable meth…
Statistical design considerations for trials that study multiple indications
Alexander M. Kaizer, Joseph S. Koopmeiners, Nan Chen +1
Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells. Innovations in clinical tr…
Multi-resolution Super Learner for Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI
Jin Jin, Lin Zhang, Ethan Leng +2
While current research has shown the importance of Multi-parametric MRI (mpMRI) in diagnosing prostate cancer (PCa), further investigation is needed for how to incorporate the spec…
Borrowing from Supplemental Sources to Estimate Causal Effects from a Primary Data Source
Jeffrey A. Boatman, David M. Vock, Joseph S. Koopmeiners
The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and exp…