teraghu

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.

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Mark Peterson

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Research in my lab occurs within a multidisciplinary and translational space that promotes greater understanding of issues in public health, clinical rehabilitation, human performance, and physiology. My specific research interests have been devoted to physical activity epidemiology and behavioral interventions for the treatment/prevention of obesity and related cardiometabolic diseases, frailty, functional motor declines, and early mortality. Although predictive models based on healthy cohorts have a certain degree of generalizability, it is necessary to better understand populations at heightened risk. Our current and future research efforts are therefore directed at understanding and identifying precision strategies to prevent metabolic dysregulation and secondary musculoskeletal pathology among children and adults with neuromuscular impairments, as well as a variety of frailty syndromes. Our primary data collection occurs through typical clinical and basic laboratory studies, high throughput imaging, and remote sensing/tracking of human movement and various biomarkers. Numerous secondary, large-data analysis efforts are also incorporated for epidemiologic studies utilizing nationally-representative samples.

 

Data science applications: Connecting statistical models and disease data; design of behavioral interventions with remote sensing and messaging; understanding pediatric cardiometabolic and muscle health; sports analytics and human performance; population-based epidemiology/surveillance; secondary disease disparities among acquired and chronic frailty syndromes

Normalized strength growth percentile curves with polynomials for men (A) and women (B) from quantile regression (Peterson and Krishnan, 2015).

Normalized strength growth percentile curves with polynomials for men (A) and women (B) from quantile regression (Peterson and Krishnan, 2015).