39 citations · 49 across the 8 of their papers we have counts for
8 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…
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…
Assessing effect heterogeneity of a randomized treatment using conditional inference trees
Ashwini Venkatasubramaniam, Brandon Koch, Lauren Erickson +3
Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and c…
Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration
A. Wentzel, P. Hanula, T. Luciani +6
We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment,…
Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data
Sunayan Bandyopadhyay, Julian Wolfson, David M. Vock +5
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used ri…
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data
Julian Wolfson, Sunayan Bandyopadhyay, Mohamed Elidrisi +5
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts…