Andrzej Galecki, MD, PhD, is Research Scientist in the department of Biostatistics, School of Public Health, and Research Professor in the Institute of Gerontology at the University of Michigan, Ann Arbor.
Dr. Raghunathan’s primary research interest is in developing methods for dealing with missing data in sample surveys and in epidemiological studies. The methods are motivated from a Bayesian perspective but with desirable frequency or repeated sampling properties. The analysis of incomplete data from practical sample surveys poses additional problems due to extensive stratification, clustering of units and unequal probabilities of selection. The model-based approach provides a framework to incorporate all the relevant sampling design features in dealing with unit and item nonresponse in sample surveys. There are important computational challenges in implementing these methods in practical surveys. He has developed SAS based software, IVEware, for performing multiple imputation analysis and the analysis of complex survey data. Raghunathan’s other research interests include Bayesian methods, methods for small area estimation, combining information from multiple surveys, measurement error models, longitudinal data analysis, privacy, confidentiality and disclosure limitations and statistical methods for epidemiological studies. His applied interests include cardiovascular epidemiology, social epidemiology, health disparity, health care utilization, and social and economic sciences. Raghunathan is also involved in the Survey Methodology Program at the Institute for Social Research, a multidisciplinary team of sociologists, statisticians and psychologists, provides an opportunity to address methodological issues in: nonresponse, interviewer behavior and its impact on the results, response or measurement bias and errors, noncoverage, respondent cognition, privacy and confidentiality issues and data archiving. The Survey Methodology Program has a graduate program offering masters and doctoral degrees in survey methodology.
Yi Li is a Professor of Biostatistics and Director of the Kidney Epidemiology and Cost Center. His current research interests are survival analysis, longitudinal and correlated data analysis, measurement error problems, spatial models and clinical trial designs. He is developing methodologies for analyzing large-scale andhigh-dimensional datasets, with direct applications inobservational studies as well in genetics/genomics. His methodologic research is funded by various federal grants starting from year 2003. Yi Li is actively involved in collaborative research in clinical trials and observational studies with researchers from the University of Michigan and Harvard University. The applications have included chronic kidney disease surveillance, organ transplantation, cancer preventive studies and cancer genomics.
My research focuses on developing statistical methods for the analysis of human genetic data and application of those methods to understand the genetic basis of human health and disease. Our methods and software are used by statisticians and geneticists worldwide. My disease research is focused on type 2 diabetes (T2D) and related traits and on bipolar disorder and schizophrenia. Our studies that are generating and analyzing genome or exome sequence data on 10,000s of individuals requiring the efficient handling of petabyte-scale data.
I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data. Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation. I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.
Dr. Song is interested in methodological developments related to modelling, statistical inference and applications in biomedical sciences. One of his current research areas concerns the development of statistical methodology and algorithm for fusion learning and homogeneity pursuit in data integration to address various analytic challenges from data heterogeneity. Another focus of his current research is on the regression analysis of networked data, with applications to electroencephalogram data analysis for the understanding of human growth and development.