Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.
Brenda Gillespie, PhD, is Associate Director in Consulting for Statistics, Computing and Analytics Research (CSCAR) with a secondary appointment as Associate Research Professor in the department of Biostatistics in the School of Public Health at the University of Michigan, Ann Arbor. She provides statistical collaboration and support for numerous research projects at the University of Michigan. She teaches Biostatistics courses as well as CSCAR short courses in survival analysis, regression analysis, sample size calculation, generalized linear models, meta-analysis, and statistical ethics. Her major areas of expertise are clinical trials and survival analysis.
Prof. Gillespie’s research interests are in the area of censored data and clinical trials. One research interest concerns the application of categorical regression models to the case of censored survival data. This technique is useful in modeling the hazard function (instead of treating it as a nuisance parameter, as in Cox proportional hazards regression), or in the situation where time-related interactions (i.e., non-proportional hazards) are present. An investigation comparing various categorical modeling strategies is currently in progress.
Another area of interest is the analysis of cross-over trials with censored data. Brenda has developed (with M. Feingold) a set of nonparametric methods for testing and estimation in this setting. Our methods out-perform previous methods in most cases.
Marcelline Harris, Ph.D., R.N., is Associate Professor of Systems, Populations and Leadership in the School of Nursing at the University of Michigan, Ann Arbor.
Dr. Harris’s research interests focus on what is being labeled the “continuous use” of clinical data (the use of clinical data for one or more purposes), computable knowledge representation strategies, and the use of electronic clinical data for practice and research. Her research has been funded by NIH, AHRQ, RWJF, and PCORI. Harris also has extensive enterprise level experience, having served in both scientific and operational positions that address the development and governance of systems that support the capture, storage, indexing, and retrieval of clinical data. At Michigan, she retains this translational perspective, emphasizing clinical data for patient-centered research, clinical surveillance and predictive analytics.
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.
Anna Kratz, PhD, is Assistant Professor of Physical Medicine and Rehabilitation and the Center for Clinical Outcomes Development and Application (CODA) at the University of Michigan, Ann Arbor.
Dr. Kratz’s clinical research is focused on the characteristics and mechanisms of common symptoms (e.g. pain, fatigue, cognitive dysfunction) and functional outcomes in those with chronic clinical conditions. Using a combination of ambulatory measurement methods of physical activity (actigraphy), heart rate variability, galvanic skin response, and self-reported experiences, her research aims to overlay the patient’s day-to-day experience with physiological markers of stress, sleep quality, and physical activity. She utilizes a number of computational approaches, including multilevel statistical modeling, signal processing, and machine learning to analyze these data. The ultimate goal is to use insights from these data to design better clinical interventions to help patients better manage symptoms and optimize functioning and quality of life.
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.
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.
Jessica K. Camp, PhD, is Assistant Professor of social work in the Department of Health and Health Services at the University of Michigan, Dearborn.
Her research focuses on using large nationally representative data from the United States and internationally (SIPP, ACS, GSOEP) to explore trends in poverty and inequality. Specifically, I examine ways that marginalized and hyper-marginalized groups experience economic disparity and labor market exclusion. My most recent completed study showed how welfare reform can have a powerful impact on the well-being of working women, especially women with vulnerabilities. My area of expertise as a data analyst is in complex samples, regression, and longitudinal models. I am hoping my future work will inform ways that “Big Data” can be used in social work research.
Our lab’s research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. We operate at the interface of physics, biology, and engineering with the primary motivation to design and develop novel approaches to unravel cancer patients’ response to chemoradiotherapy treatment by integrating physical, biological, and imaging information into advanced mathematical models using combined top-bottom and bottom-top approaches that apply techniques of machine learning and complex systems analysis to first principles and evaluating their performance in clinical and preclinical data. These models could be then used to personalize cancer patients’ chemoradiotherapy treatment based on predicted benefit/risk and help understand the underlying biological response to disease. These research interests are divided into the following themes:
- Bioinformatics: design and develop large-scale datamining methods and software tools to identify robust biomarkers (-omics) of chemoradiotherapy treatment outcomes from clinical and preclinical data.
- Multimodality image-guided targeting and adaptive radiotherapy: design and develop hardware tools and software algorithms for multimodality image analysis and understanding, feature extraction for outcome prediction (radiomics), real-time treatment optimization and targeting.
- Radiobiology: design and develop predictive models of tumor and normal tissue response to radiotherapy. Investigate the application of these methods to develop therapeutic interventions for protection of normal tissue toxicities.
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.