Trivellore Raghunathan

Director, Survey Research Center, Institute for Social Research.
Research Professor, Survey Research Center, Institute for Social Research.
Professor, Department of Biostatistics, School of Public Health.

Ph.D., Statistics, Harvard University

Research Interests: Dr. Raghunathan's research interests are in the analysis of incomplete data, multiple imputation, Bayesian methods, design and analysis of sample surveys, small area estimation, confidentiality and disclosure limitation, longitudinal data analysis and statistical methods for epidemiology.

teraghu@umich.edu

734-763-1456

News & Announcements

Raghunathan et al. found behavioral treatment reduces urinary incontinence

Raghunathan's new book incorporates practical guidance and analytic resources for using multiple imputation to address missing data problems

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MacKinnon, Neil, Trivellore Raghunathan, Wencheng Ge, Peisong Han, Javed Siddiqui, John T. Wei, Arul M. Chinnaiyan, Thekkelnaycke M. Rajendiran, an, et al. 2019. "NMR-Based Metabolomic Profiling of Urine: Evaluation for Application in Prostate Cancer Detection." Natural Product Communications, 14(5).

Siddique, Juned, Michael J. Daniels, Raymond J. Carroll, Trivellore Raghunathan, Elizabeth A. Stuart, and Laurence S. Freedman. Forthcoming. "Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study." Biometrics.

Cutler, David M., Kaushik Ghosh, Kassandra Lynn Messer, Trivellore Raghunathan, Susan T. Stewart, and Allison Rosen. 2019. "Explaining The Slowdown In Medical Spending Growth Among The Elderly, 1999-2012." Health Affairs, 38(2): 222-229.

Sakshaug, Joseph W., Alexandru Cernat, and Trivellore Raghunathan. Forthcoming. "Do Sequential Mixed-Mode Surveys Decrease Nonresponse Bias, Measurement Error Bias, and Total Bias? An Experimental Study." Journal of Survey Statistics and Methodology.

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Collaborative Research: GCR: The Future of Quantitative Research in Social Science (NSF)

Health, Wealth, and Time Use Over the Life Course and Across Generations (NIA)

Computing Statistics from Private Data (Sloan)

Addressing Disclosure Risk of Contextualized Microdata in Survey Design (OBSSR)

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