Lane F. Burgette

Photo of Lane Burgette
Statistician
Pittsburgh Office

Education

Ph.D. in statistics, University of Wisconsin

Overview

Lane Burgette is a statistician at the RAND Corporation. His focus is Bayesian model building for applications in the health and social sciences. Burgette's methodological interests include models of categorical outcomes, causal inference, methods for missing data, and nonparametric modeling. His applied research interests include physician payment policy; cost, quality, and value of health care; and treatment for alcohol and other drug abuse. Burgette was trained at Whitman College, the University of Wisconsin-Madison, and Duke University and has taught at Duke and Johns Hopkins Universities. Burgette received his Ph.D. in statistics from the University of Wisconsin.

Selected Publications

Mulcahy, A.W., B. Wynn, L. Burgette, and A. Mehrotra, "Medicare’s step back from global periods — Unbundling postoperative care," New England Journal of Medicine, 372(15), 2015

LF Burgette and JP Reiter, "Nonparametric Bayesian multiple imputation for missing data due to mid-study switching of measurement methods," Journal of the American Statistical Association, 107(498), 2012

LF Burgette and EV Nordheim, "The trace restriction: An alternative identification strategy for the Bayesian multinomial probit model," Journal of Business and Economic Statistics, 30(3), 2012

LF Burgette, JP Reiter, and ML Miranda, "Exploratory quantile regression with many covariates: An application to adverse birth outcomes," Epidemiology, 22(6), 2011

LF Burgette and JP Reiter, "Multiple imputation for sequential regression trees," American Journal of Epidemiology, 172(9), 2010

Burgette, L.F., A.W. Mulcahy, A. Mehrotra, T. Ruder, and B.O. Wynn, "Estimating surgical procedure times using anesthesia billing data and operating room records," Health Services Research (forthcoming)

LF Burgette and SM Paddock, "Bayesian models for semicontinuous outcomes in rolling admission therapy groups," Psychological Methods, 22(4), 2018

CM Setodji, DF McCaffrey, LF Burgette, D Almirall, and BA Griffin, "The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores," Epidemiology, 28(6), 2017

Publications