Jamie Perin, PhD
615 N. Wolfe St.
Baltimore , Maryland
PhD , 2009
My background is focused on statistical and quantitative research methodology in public health, including longitudinal and incomplete data, semi-parametric models, categorical data, clustered data, survival analysis, multiple imputation, and the EM algorithm. I am driven principally by statistics in the service of international health research. My current projects include modeling child mortality and the associations among infectious diseases, especially diarrhea and pneumonia relating to child health.
In global health and for underserved communities, what drives research and ultimately influences policy is founded on principals and assumptions that are quantitatively demonstrable. I believe quantitative methods serve public health by facilitating evaluation and objectivity. Likewise, I hope to contribute to the health of global communities through statistical application in the collaborative research environment.
international health, biostatistics, longitudinal data, missing data, child mortality, statistical epidemiology, diarrhea, child health
Li Liu, Qingfeng Li, Rose A. Lee, Ingrid K. Friberg, Jamie Perin, Neff Walker, Robert E. Black. 2011. “Trends in causes of death among children under 5 in Bangladesh, 1993-2004: an exercise applying a standardized computer algorithm to assign causes of death using verbal autopsy data”. Population Health Metrics. 9(1):43
Preisser J, Phillips C, Perin J, Schwartz T. 2010. Regression models for patient-reported measures having ordered categories recorded on multiple occasions. Community Dentistry and Oral Epidemiology (in press)
Perin J, Preisser J, Rathouz P. Semi-parametric efficient estimator for incomplete longitudinal binary data with application to smoking trends. 2009. Journal of the American Statistical Association 104(488):1373-1384.
Preisser, J.S., Qaqish, B., Perin, J. 2008. A note on deletion diagnostics for estimating equations. Biometrika, 95(2):509-513.
Preisser, J.S., Perin, J. 2007. Deletion diagnostics for marginal mean and correlation model parameters in estimating equations. Statistics and Computing, 17(4):381-393.