Anna Kratz

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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.


Trivellore E. Raghunathan

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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.