Lane Burgette is a senior statistician at the RAND Corporation. He is also a faculty member at Pardee RAND Graduate School. His research focus is causal inference and Bayesian modeling in the health and social sciences.
Burgette's methodological interests include models of categorical outcomes, propensity score methods, 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.
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
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)