Brent Johnson Lab
Welcome to the Brent Johnson Lab
The Brent Johnson Lab researches statistical methodology for biomedical and public health applications including semi-parametric and non-parametric methods in missing data problems, measurement error, and survival analysis. A substantial part of the current research program is dedicated toward drawing statistical inference from observational data. We have also investigated regularized estimation and variable selection for practical problems that arise in collaborations. Research funding supported by grants from the National Institutes of Health.
![Huber’s acting on mark-scale residuals "V when E["V |"T = z] is a sinusoidal function in z.](/MediaLibraries/URMCMedia/labs/johnson-lab/images/fig1-rmmark.jpg)
Brent A. Johnson, Ph.D.
Principal Investigator
Publications
2018
Brust CMJ, Shah S, Mlisana K, Moodley P, Allana S, Campbell A, Johnson BA, Master I, Mthiyane T, Lachman S, Larkan L-M, Ning Y, Malik A, Smith JP, Gandhi NR (2018) Improved survival and cure rates with concurrent treatment for MDR-TB/HIV co-infection in KwaZulu-Natal, South Africa. Clinical Infectious Disease (In press)
Rice J, Johnson BA, Strawderman RL (2018) Modeling the rate of HIV testing from repeated binary data among potential never-testers. Biostatistics (In press)
Rice J, Strawderman RL, Johnson BA (2018) Regularity of a renewal process estimated from binary data. Biometrics (In press)
McIntyre J, Johnson BA, Rappaport SM (2018) Monte Carlo methods for nonparametric regression with heteroscedastic measurement error. Biometrics (In press)
Contact Us
Brent Johnson Lab
Sauders Research Building
265 Crittenden Blvd.
Rochester, NY 14642
(585) 273-1031