3 citations · 7 across the 4 of their papers we have counts for
6 papers · 1 filter
Bayesian Nonparametric Cost-Effectiveness Analyses: Causal Estimation and Adaptive Subgroup Discovery
Arman Oganisian, Nandita Mitra, Jason Roy
Cost-effectiveness analyses (CEAs) are at the center of health economic decision making. While these analyses help policy analysts and economists determine coverage, inform policy,…
Net benefit separation and the determination curve: a probabilistic framework for cost-effectiveness estimation
Andrew J. Spieker, Nicholas Illenberger, Jason A. Roy +1
Considerations regarding clinical effectiveness and cost are essential in comparing the overall value of two treatments. There has been growing interest in methodology to integrate…
Bayesian Longitudinal Causal Inference in the Analysis of the Public Health Impact of Pollutant Emissions
Chanmin Kim, Corwin M Zigler, Michael J Daniels +2
Pollutant emissions from coal-burning power plants have been deemed to adversely impact ambient air quality and public health conditions. Despite the noticeable reduction in emissi…
A Bayesian Nonparametric Model for Zero-Inflated Outcomes: Prediction, Clustering, and Causal Estimation
Arman Oganisian, Nandita Mitra, Jason Roy
Researchers are often interested in predicting outcomes, conducting clustering analysis to detect distinct subgroups of their data, or computing causal treatment effects. Pathologi…
Outcome identification in electronic health records using predictions from an enriched Dirichlet process mixture
Bret Zeldow, James Flory, Alisa Stephens-Shields +2
We propose a novel semiparametric model for the joint distribution of a continuous longitudinal outcome and the baseline covariates using an enriched Dirichlet process (EDP) prior.…
Bayesian nonparametric generative models for causal inference with missing at random covariates
Jason Roy, Kirsten J Lum, Michael J. Daniels +3
We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and…