Dr. Paul Albert's research interests primarily focus on complex modeling of correlated outcomes in biomedical sciences, including the analysis of longitudinal data, diagnostic testing, and data from biomarker studies.
He also develops new methodological techniques for predicting future disease progression or poor outcomes from longitudinally collected biomarkers, including Markov modeling techniques (Hidden Markov) for recurrent events with misclassification.
Dr. Albert was appointed senior investigator and chief of the Biostatistics Branch, Division of Cancer Epidemiology & Genetics in 2016. Prior to joining the Division, Dr. Albert was senior investigator and chief of Biostatistics and Bioinformatics Branch in the Division of Epidemiology, Statistics, and Prevention in the Eunice Kennedy Shriver National Institute of Child Health and Human Development. He came to the NIH in 1998, first as a staff fellow in the National Institute of Neurological Disorders and Stroke in the Biometry and Field Studies Branch, later as a mathematical statistician in the National Heart Lung and Blood Institute, and the Division of Cancer Treatment and Diagnosis. Dr. Albert received his Ph.D. in biostatistics from the Johns Hopkins University.
- Cheon K, Thoma ME, Kong X, Albert PS. A mixture of transition models for heterogeneous longitudinal ordinal data: with applications to longitudinal bacterial vaginosis data. Stat Med. 2014;33(18):3204-13.
- Albert PS, Liu A, Nansel T. Efficient logistic regression designs under an imperfect population identifier. Biometrics. 2014;70(1):175-84.
- Malinovsky Y, Albert PS, Schisterman EF. Pooling designs for outcomes under a Gaussian random effects model. Biometrics. 2012;68(1):45-52.
- McLain AC, Albert PS. Modeling longitudinal data with a random change point and no time-zero: applications to inference and prediction of the labor curve. Biometrics. 2014;70(4):1052-60.
Related Scientific Focus Areas
This page was last updated on Monday, December 5, 2022