1 citations · 1 across the 4 of their papers we have counts for
7 papers
A regression framework for a probabilistic measure of cost-effectiveness
Nicholas Illenberger, Nandita Mitra, Andrew J. Spieker
To make informed health policy decisions regarding a treatment, we must consider both its cost and its clinical effectiveness. In past work, we introduced the net benefit separatio…
Analysis of survival data with non-proportional hazards: A comparison of propensity score weighted methods
Elizabeth A. Handorf, Marc Smaldone, Sujana Movva +1
One of the most common ways researchers compare survival outcomes across treatments when confounding is present is using Cox regression. This model is limited by its underlying ass…
Doubly Robust Nonparametric Instrumental Variable Estimators for Survival Outcomes
Youjin Lee, Edward H. Kennedy, Nandita Mitra
Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or conti…
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…
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…