Brian G. Vegetabile

Statistician

Santa Monica Office

Education

Ph.D. in statistics, UC Irvine; M.S. in statistics, UC Irvine; B.S. in aerospace engineering, Penn State University

One Page Bio

Overview

Brian Vegetabile is a statistician at the RAND Corporation. His current methodological research interests are in Bayesian inference and statistical machine learning techniques as they apply to causal inference in observational studies. His prior research was motivated by problems arising from the Conte Center on Brain Programming and Adolescent Vulnerabilities at the University of California, where he also studied methods for quantifying predictability in mother-infant interactions as a marker for later behavioral outcomes in observational data settings. Vegetabile earned a B.S. in aerospace engineering from Penn State University and spent four years working as a satellite systems engineer for Northrop Grumman. His Ph.D. thesis focused on methods for obtaining optimal covariate balance for causal inference in observational studies. He obtained his Ph.D. in statistics from the University of California, Irvine.

Research Focus

Concurrent Non-RAND Positions

Associate Editor, Annals of Applied Staitstics

Previous Positions

Satellite Systems Engineer, Northrop Grumman; Systems Engineer, Northrop Grumman

Selected Publications

Brian G. Vegetabile, Beth Ann Griffin, Donna L. Coffman, Matthew Cefalu, Michael W. Robbins & Daniel F. McCaffrey, "Nonparametric estimation of population average dose-response curves using entropy balancing weights for continuous exposures," Health Services and Outcomes Research Methodology, 21, 2021

Terry L Schell, Samuel Peterson, Brian G. Vegetabile, Adam Scherling, Rosanna Smart, Andrew R Morral, State-level estimates of household firearm ownership, RAND Corporation (TL-354), 2020

Beth Ann Griffin, Lynsay Ayer, Joseph Pane, Brian Vegetabile, Lane Burgette, Daniel McCaffrey, Donna L Coffman, Matthew Cefalu, Rod Funk, Mark D Godley, "Expanding outcomes when considering the relative effectiveness of two evidence-based outpatient treatment programs for adolescents," Journal of Substance Abuse Treatment, 118, 2020

Brian G. Vegetabile, Daniel L. Gillen, Hal S. Stern, "Optimally balanced Gaussian process propensity scores for estimating treatment effects," Journal of the Royal Statistical Society, Series A - Statistics in Society, 183, 2020

Brian G Vegetabile, Stephanie A Stout-Oswald, Elysia Poggi Davis, Tallie Z Baram, Hal S Stern, "Estimating the entropy rate of finite Markov Chains with application to behavior studies," Journal of Educational and Behavioral Statistics, 44(3), 2019

Honors & Awards

UAI Top Reviewer, Uncertainty in Artificial Intelligence
ICML Top Reviewer, International Conference on Machine Learning

Publications

Research Brief
New Patient-Centered Quality Measures for Office-Based Palliative Care
This research brief describes the development and testing of two patient-centered quality measures of symptom management and communication and provides considerations for palliative care providers that plan to use them.
Sep 13, 2021
Report
MACRA Palliative Care Quality Measure Development—Testing Summary Report: Measure Name: Receiving Desired Help for Pain
The authors describe the results of their national beta field test for a performance measure designed to assess the extent to which patients who used ambulatory palliative care received the help they wanted for their pain.
Sep 3, 2021
Report
MACRA Palliative Care Quality Measure Development—Testing Summary Report: Measure Name: Feeling Heard and Understood
The authors describe the results of their national beta field test for a performance measure designed to assess the extent to which patients who used ambulatory palliative care felt heard and understood by their palliative care provider and team.
Sep 3, 2021
Tool
Toolkit for Weighting and Analysis of Nonequivalent Groups: A Tutorial on the TWANG Shiny App for Two Treatments
This tutorial describes how to use a menu-driven Shiny app based on the Toolkit for Weighting and Analysis of Nonequivalent Groups (TWANG) R package. It can be used to estimate propensity score weights and treatment effects for binary treatments.
Jun 30, 2020
Draft
MACRA Palliative Care Quality Measure Development: Summary Report of Alpha Testing
Summarizes findings from a small pilot study which was conducted to evaluate the feasibility of data collection processes and refine the testing plan in advance of a larger national field test.
Jun 15, 2020
Multimedia
State-of-the-Art Strategies for Addressing Selection Bias When Comparing Two or More Treatment Groups
This course introduces causal modeling using the potential outcomes framework and propensity score weights to estimate causal effects from observational data.
May 1, 2020
Tool
State-Level Estimates of Household Firearm Ownership
As part of the Gun Policy in America initiative, researchers developed annual, state-level estimates of household firearm ownership from 1980 to 2016, which should help further research on the relationship between firearm ownership and key outcomes.
Apr 22, 2020
Journal Article
Optimally Balanced Gaussian Process Propensity Scores for Estimating Treatment Effects
This paper introduces a new approach to estimating the propensity score using Gaussian processes and optimizing hyperparameters with respect to covariate balance.
Sep 19, 2019