Andrzej Galecki is a Research Professor in the Institute of Gerontology and a Research Scientist in the Department of Biostatistics. He earned his Ph.D. from the Institute of Mother and Child Care, Warsaw (1987). His research interests include application of modern statistical methods to studies in geriatrics and gerontology. In particular he is interested in nonlinear mixed effects models, population PK/PD analysis, modeling of covariance structure in longitudinal data analysis, generalized linear models and categorical data analysis.
Dr. Galecki is a Director of the Analysis Core in the "Genetics of Age-Sensitive Traits in Mice" Program Project, Co-Director for the Methodology, Data Management and Analysis Core and for Genomics and Analysis Core of the Older Americans Independence Center.
- Ph.D., Epidemiology, Institute of Mother and Child Care in Warsaw (Poland), 1987
- Physician Diploma, Medicine, Medical Academy of Warsaw, 1981
- M.S., Applied Mathematics, Technical University of Warsaw, 1977
Research Interests & Projects
- My primary interests lie in developing computational methods for analyzing correlated and over dispersed data, which are frequently encountered in many fields of application, such as pharmacokinetic and pharmacodynamic (PK/PD) studies, longitudinal studies, survey sampling and gene mapping in genetics studies. A class of models often considered in this context are hierarchical or mixed effects models. These models are an extension of the regression models whereby random effects are introduced to describe between-subject variation. My particular interests related to mixed effects models lie in: 1. An extension of mixed models which allows between-subject variation to be modeled as a mixture of underlying distributions. This type of model can be applied to interval and composite gene mapping of quantitative and qualitative trait loci in experimental animal crosses. 2. Computational methods for advanced PK/PD population studies. Here models are often expressed as a solution of a system of ordinary differential equations. To address these problems a new SAS/IML NLMEM macro has been developed and successfully used to analyze existing data. Specific application of these models occurs in population studies when intravenous glucose tolerance test (IVGTT) studies are used to evaluate glucose metabolism in patients. 3. Modeling covariance structure in the presence of two or more repeated factors. A class of models proposed in Galecki, 1994 has been implemented in PROC/MIXED, which is part of commercial statistical software SAS, starting with version 6.12. The proposed class of covariance structures is especially useful for the analysis of several outcomes measured over time. My other research interests involve a wide range of methodological and practical aspects of research on elderly including study design and conducting study itself. I am involved in a number of large NIH funded projects including analysis cores at the Claude D. Pepper Older Americans Independence Center and Genetics of Age-Sensitive Traits in Mice Program project.
- Wang, Y., Eskridge, K., Galecki, A.T. (2010). Bayesian Analysis of Minimal Model under the Insulin-Modified IVGTT Journal of Health 188-194..
- Wang, Y., Eskridge, K., Nadarajah, S., Galecki, A.T. (2009). Bayesian and non-Bayesian analysis of mixed-effects PK/PD models based on differential equations Monte Carlo Methods Applications 145-167.
- Galecki, A.T., Chen.S., Faulkner, J., Ashton-Miller, J. , Burzykowski,T. (2009). Statistical Power Calculations for Clustered Data International Journal of Knowledge Engineering and Soft Data Paradigms (KESDP) 40-48.
- Galecki, A.T., Wolfinger, R.D., Linares, O.A., Smith, M.J., Halter, J.B. (2004). Ordinary differential equation PK/PD models using the SAS macro NLINMIX. Journal of Biopharmaceutical Statistics 483-503.
- American Statistical Association
- International Biometric Society, ENAR