Kevin Dombkowski

By |

Kevin Dombkowski, DrPH, is Research Associate Professor in the department of Pediatrics, Medical School, and holds a secondary appointment in the School of Public Health at the University of Michigan, Ann Arbor.

Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions. Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems. Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.

Further, his research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems. He has led numerous collaborations with the Michigan Department of Health and Human Services, including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR). Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic.

Rie Suzuki

By |

Dr. Suzuki is a behavioral scientist and has major research interests in examining and intervening mediational social determinants factors of health behaviors and health outcomes across lifespan. She analyzes the National Health Interview Survey, Medical Expenditure Panel Survey, National Health and Nutrition Examination Survey as well as the Flint regional medical records to understand the factors associating with poor health outcomes among people with disabilities including children and aging.

Romesh P. Nalliah

By |

Dr. Nalliah’s expertise in research focuses on process evaluation. He has studied healthcare delivery processes, educational processes and healthcare outcomes. Dr. Nalliah’s research studies were the first time nationwide data was used to highlight hospital resource utilization for managing dental caries, pulpal lesions, periapical lesions and general oral conditions in the United States. Dr. Nalliah is internationally recognized as a pioneer in the field of nationwide hospital dataset research for dental conditions and has numerous publications in peer reviewed journals.

Dr. Nalliah’s interest for future research is to expand experience in various areas of public health but not forget his connection to dentistry. Dr. Nalliah has conducted research related to gun violence, facial fractures, spinal fusion, oral cancer, dental conditions, educational debt, mental health, suicide, sports injuries, poisoning and the characteristics of patients discharged against medical advice. National recognition of his expertise in these broader topics of medicine have given rise to opportunities to lecture to medical residents, nurse practitioners, students in medical, pharmacy and nursing programs about oral health. This is his passion- that his research should inform an evolution of dental and health education curriculum and practice.

Dr. Nalliah’s passion in research is improving healthcare delivery systems and he’s interested in improving processes, minimizing inefficiencies, reducing healthcare bottlenecks, increasing quality, and increase task sharing which will lead to a patient-centered, coherent healthcare system. Dr. Nalliah’s research has identified systems constraints and his goal is to influence policy and planning to break those constraints and improve healthcare delivery.

Michael Elliot

By |

Michael Elliott, PhD, is a Professor of Biostatistics, School of Public Health, and Research Scientist at the Institute for Social Research at the University of Michigan, Ann Arbor.

Dr. Elliott’s statistical research interests focus around the broad topic of “missing data,” including the design and analysis of sample surveys, casual and counterfactual inference, and latent variable models. He has worked closely with collaborators in injury research, pediatrics, women’s health, and the social determinants of physical and mental health. Dr. Elliott serves as an Associate Editor for the Journal of the American Statistical Association.

Jeremy M G Taylor

By |

Jeremy Taylor, PhD, is the Pharmacia Research Professor of Biostatistics in the School of Public Health and Professor in the Department of Radiation Oncology in the School of Medicine at the University of Michigan, Ann Arbor. He is the director of the University of Michigan Cancer Center Biostatistics Unit and director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England; the University of Adelaide; INSERM, Bordeaux and CSIRO, Sydney, Australia. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.

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.

Hongwei Xu

By |

My substantive research interest is to understand the role of geography in shaping population health. Towards this end, my methodological and data science interests are twofold. First, I seek to develop and apply spatial statistical methods to model individual- and area-level health and diseases by using survey data and government statistics. Second, in light of the advance in GIS techniques and the increasingly accessible spatial data from various sources, I am exploring new approaches to integrate traditional geo-referenced survey data with non-traditional spatial data (e.g., remote sensing data, satellite data, Google search) to reduce measurement errors in demographic health research.

Pamela Davis-Kean

By |

Pamela Davis-Kean, PhD, is Professor of Psychology, College of Literature, Science, and the Arts, and Research Professor, Survey Research Center and Research Center for Group Dynamics, Institute for Social Research, at the University of Michigan, Ann Arbor.

Prof. Davis-Kean is the Director of the Population, Neurodevelopment, and Genetics program at the Institute for Social Research. This group examines the complex transactions of brain, biology, and behavior as children and families develop across time. She is interested in both micro (brain and biology) and macro (family and socioeconomic conditions) aspects of development to understand the full developmental story of individuals.  Her primary focus in this area is how stress relates to family socioeconomic status and how that translates to parenting beliefs and behaviors that influence the development of children.

Kerby Shedden

By |

Kerby Shedden, PhD, is Professor of Statistics, College of Literature, Science, and the Arts, Professor of Biostatistics, School of Public Health, and Director of the Consulting for Statistics, Computing, and Analytics Research (CSCAR) center.

Kerby Shedden received his PhD in Statistics from UCLA in 1999 and joined the University of Michigan the same year.  His research interests include genomics, genetics, and other areas of life science where large and complex data arise. He also is interested in computational statistics and statistical software development. He participates in many collaborative research efforts including biomarker screening for cancer and kidney disease outcomes, cell-based screening for understanding the behavior of chemical probes in cells, and genetic association analysis for longitudinal traits.

Daniel Brown

By |

Daniel Brown, PhD, is Professor in the School of Environment and Sustainability and holds a secondary appointment as Research Professor in the Survey Research Center, Institute for Social Research. Prof. Brown is also Director of the Environmental Spatial Analysis Laboratory.

Prof. Brown’s research interests focus on land use change and its effects on ecosystems and on human vulnerability. This work connects a computer-based simulation (e.g., agent-based modeling) of land-use-change processes with GIS and remote sensing based data on historical patterns of landscape change and social surveys. Brown and colleagues are working to couple these models with GIS-based data and other models to evaluate consequences of change. They are also working to understand the ways in which land-use decisions are made. Collaborative research investigate the effects of spatial and social neighborhoods on the physical and social risks on human health.

Though most of Professor Brown’s earlier work has been in the US, his work is becoming increasingly international, with projects in China, Africa, and India.

Research on land-cover and land-use change is funded by the NASA Land-Cover Land-Use Change Program and by programs at the National Science Foundation on Human and Social Dynamics (HSD) and the Dynamics of Coupled Natural and Human Systems (CNH) and conducted in collaboration with colleagues in SEAS and in the Center for the Study of Complex Systems. Research on spatial aspects of public health is conducted in collaboration with colleagues in the School of Public Health and funded by the National Institutes of Health, the Robert Wood Johnson Foundation, and the US Environmental Protection Agency.

Roderick Little

By |

Roderick Joseph Little, PhD, is the Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, Research Professor, Institute for Social Research, and Senior Fellow, Michigan Society of Fellows, at the University of Michigan, Ann Arbor.

Prof. Little’s primary research interest is the analysis of data sets with missing values. Many statistical techniques are designed for complete, rectangular data sets, but in practice biostatistical data sets contain missing values, either by design or accident. As detailed in my book with Rubin, initial statistical approaches were relatively ad-hoc, such as discarding incomplete cases or substituting means, but modern methods are increasingly based on models for the data and missing-data mechanism, using likelihood-based inferential techniques.

Another interest is the analysis of data collected by complex sampling designs involving stratification and clustering of units. Since working as a statistician for the World Fertility Survey, I have been interested in the development of model-based methods for survey analysis that are robust to misspecification, reasonably efficient, and capable of implementation in applied settings. Statistics is philosophically fascinating and diverse in application. My inferential philosophy is model-based and Bayesian, although the effects of model misspecification need careful attention. My applied interests are broad, including mental health, demography, environmental statistics, biology, economics and the social sciences as well as biostatistics.