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Kevin Dombkowski

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Kevin J. Dombkowski, DrPH., MS, is Research Professor with the Child Health Evaluation and Research (CHEAR) Center within the University of Michigan Department of Pediatrics.   He is a health services researcher working extensively with public health information systems and large administrative claims databases.  

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.  Kevin is currently collaborating with Michigan State University on the design and development of the Flint Lead Exposure Registry (FLExR) information architecture.

Kevin’s 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 (MDHHS), 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.  Kevin is currently collaborating with MDHHS to evaluate MCIR data quality as immunization providers across Michigan adopt real-time, bi-directional messaging between electronic health records and MCIR.   He is conducting a similar statewide evaluation as new messaging protocols are adopted by electronic laboratory systems for reporting blood lead testing results to MDHHS.

Adriene Beltz

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The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

Kai S. Cortina

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Kai S. Cortina, PhD, is Professor of Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Cortina’s major research revolves around the understanding of children’s and adolescents’ pathways into adulthood and the role of the educational system in this process. The academic and psycho-social development is analyzed from a life-span perspective exclusively analyzing longitudinal data over longer periods of time (e.g., from middle school to young adulthood). The hierarchical structure of the school system (student/classroom/school/district/state/nations) requires the use of statistical tools that can handle these kind of nested data.

 

Andrew Grogan-Kaylor

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My core intellectual interest is the way in which parenting behaviors, like the use of physical punishment, or parental expressions of emotional warmth, have an effect on child outcomes like aggression, antisocial behavior, anxiety and depression, and how these dynamics play out across contexts, neighborhoods, and cultures.  A lot of my work is done with international samples. In my work I use statistical models, like multilevel models and some econometric models, and software like Stata, R, HLM and ArcGIS, to examine things like growth and change over time, or community, school or parent effects on children and families.  I have emerging interests in text-mining and natural language processing.

Visualization of multilevel modeling using High School and Beyond data set.

Visualization of multilevel modeling using High School and Beyond data set.

Joseph Ryan

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Joseph Ryan, PhD, is Associate Professor of Social Work, School of Social Work and Faculty Associate in the Center for Political Studies, ISR, at the University of Michigan, Ann Arbor.

Prof. Ryan’s research and teaching build upon his direct practice experiences with child welfare and juvenile justice populations. Dr. Ryan is the Co-Director of the Child and Adolescent Data, an applied research center focused on using big data to drive policy and practice decisions in the field. Dr. Ryan is currently involved with several studies including a randomized clinical trial of recovery coaches for substance abusing parents in Illinois (AODA Demonstration), a foster care placement prevention study for young children in Michigan (MiFamily Demonstration), a Pay for Success (social impact bonds) study focused on high risk adolescents involved with the Illinois child welfare and juvenile justice system and a study of the educational experiences of youth in foster care (Kellogg Foundation Education and Equity). Dr. Ryan is committed to building strong University and State partnerships that utilize big data and data visualization tools to advance knowledge and address critical questions in the fields of child welfare and juvenile justice.

Joseph Himle

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Joseph A. Himle, PhD, is the Associate Dean for Research and the Howard V. Brabson Collegiate Professor of Social Work, School of Social Work, and Professor of Psychiatry, Medical School, at the University of Michigan, Ann Arbor.

The goal of Prof. Himle’s research is to design, develop and test a inconspicuous, awareness-enhancement and monitoring device (AEMD) which will assist the treatment of trichotillomania (TTM), a disorder involving recurrent pulling of one’s hair resulting in noticeable hair loss. TTM is associated with significant impairments in social functioning and often has a profound negative impact on self-esteem and well being. Best practice treatment for TTM involves a form of behavioral therapy known as habit reversal therapy (HRT). HRT requires persons with trichotillomania to be aware of their hair pulling behaviors, yet the majority of persons with TTM pull most of their hair outside of their awareness . HRT also requires TTM sufferers to record the frequency and duration of their hair pulling behaviors yet it is obviously impossible for a person to monitor behaviors that they are unaware of. Our Phase I efforts have produced a prototype device (AEMD) that solves these two problems. The prototype AEMD signals the TTM sufferer if their hand approaches their hair, thereby bringing pulling-related behavior into awareness. The prototype AEMD also logs the time, date, duration, and user classification of hair pulling related events and can later transfer the logged data to a personal computer for analysis and data presentation. He continues to refine this device and seek to integrate it with smart-phones to better understand activities and locations associated with hair pulling or other body-focused repetitive behaviors (e.g., skin picking). In the future, he seeks to pool data from users to get a better sense of common situations and other factors associated with elevated pulling rates. He intends to develop other electronic tools to detect, monitor and intervene with other mental disorders in the future.

Anuj K. Pradhan

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I am interested in the etiology of injuries and fatalities due to motor vehicle crashes from a human factors and behavioral standpoint. I conduct research using a variety of methodologies in order to uncover and disseminate evidence that can contribute to the safe mobility of road users, that can inform policy, and that can lead to technological and educational innovations for improving the road safety record and for reducing injury. My research covers driver behavior and traffic safety with a focus on automated and connected vehicles, young and novice drivers risk behaviors; training and intervention; and, distraction detection and mitigation. My research projects in these areas use various approaches and methodologies including driving simulation, test tracks, naturalistic methods, and observational methods.

Michael Elliott

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Michael Elliott is Professor of Biostatistics at the University of Michigan School of Public Health and Research Scientist at the Institute for Social Research. 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. He is currently serving as a co-investigator on the MIDAS-affiliated Reinventing Urban Transportation and Mobility project, working to develop methods to improve the representativeness of naturalistic driving data.

Johann Gagnon-Bartsch

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Johann Gagnon-Bartsch, PhD, is Assistant Professor of Statistics in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Gagnon-Bartsch’s research currently focuses on the analysis of high-throughput biological data as well as other types of high-dimensional data. More specifically, he is working with collaborators on developing methods that can be used when the data are corrupted by systematic measurement errors of unknown origin, or when the data suffer from the effects of unobserved confounders. For example, gene expression data suffer from both systematic measurement errors of unknown origin (due to uncontrolled variations in laboratory conditions) and the effects of unobserved confounders (such as whether a patient had just eaten before a tissue sample was taken). They are developing methodology that is able to correct for these systematic errors using “negative controls.” Negative controls are variables that (1) are known to have no true association with the biological signal of interest, and (2) are corrupted by the systematic errors, just like the variables that are of interest. The negative controls allow us to learn about the structure of the errors, so that we may then remove the errors from the other variables.

Microarray data from tissue samples taken from three different regions of the brain (anterior cingulate cortex, dorsolateral prefrontal cortex, and cerebellum) of ten individuals. The 30 tissue samples were separately analyzed in three different laboratories (UC Davis, UC Irvine, U of Michigan). The left plot shows the first two principal components of the data. The data cluster by laboratory, indicating that most of the variation in the data is systematic error that arises due to uncontrolled variation in laboratory conditions. The second plot shows the data after adjustment. The data now cluster by brain region (cortex vs. cerebellum). The data is from GEO (GSE2164).

Microarray data from tissue samples taken from three different regions of the brain (anterior cingulate cortex, dorsolateral prefrontal cortex, and cerebellum) of ten individuals. The 30 tissue samples were separately analyzed in three different laboratories (UC Davis, UC Irvine, U of Michigan). The left plot shows the first two principal components of the data. The data cluster by laboratory, indicating that most of the variation in the data is systematic error that arises due to uncontrolled variation in laboratory conditions. The second plot shows the data after adjustment. The data now cluster by brain region (cortex vs. cerebellum). The data is from GEO (GSE2164).

Daniel Almirall

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Daniel Almirall, Ph.D., is Assistant Professor in the Survey Research Center and Faculty Associate in the Population Studies Center in the Institute for Social Research at the University of Michigan.

Prof. Almirall’s current methodological research interests lie in the broad area of causal inference. He is particularly interested in methods for causal inference using longitudinal data sets in which treatments, covariates, and outcomes are all time-varying. He is also interested in developing statistical methods that can be used to form adaptive interventions, sometimes known as dynamic treatment regimes. An adaptive intervention is a sequence of individually tailored decisions rules that specify whether, how, and when to alter the intensity, type, or delivery of treatment at critical decision points in the medical care process. Adaptive interventions are particularly well-suited for the management of chronic diseases, but can be used in any clinical setting in which sequential medical decision making is essential for the welfare of the patient. They hold the promise of enhancing clinical practice by flexibly tailoring treatments to patients when they need it most, and in the most appropriate dose, thereby improving the efficacy and effectiveness of treatment.

Study Design Interests: In addition to developing new statistical methodologies, Prof. Almirall devotes a portion of his research to the design of sequential multiple assignment randomized trials (SMARTs). SMARTs are randomized trial designs that give rise to high-quality data that can be used to develop and optimize adaptive interventions.

Substantive Interests: As an investigator and methodologist in the Institute for Social Research, Prof. Almirall takes part in research in a wide variety of areas of social science and treatment (or interventions) research. He is particularly interested in the substantive areas of mental health (depression, anxiety) and substance abuse, especially as related to children and adolescents.