Trivellore Eachambadi Raghunathan, PhD
- Professor, Biostatistics
- Research Professor, Survey Research Center, Institute for Social Research
Trivellore Raghunathan (Raghu) is a Professor of Biostatistics and the Director and Research Professor at the Institute for Social Research. He is a Research Professor in the Joint Program in Survey Methodology at the University of Maryland. He is the Director of Biostatistics Collaborative and Methodology Research Core (BCMRC), a research unit designed to foster collaborative and methodological research with the researchers in other departments in the School of Public Health and other allied schools. He is the Director of Biostatistics and Measurement Core for the Michigan CTSA located at Michigan Institute for Clinical and Health Research (MICHR). He is an Associate Director of the Center for Research on Ethnicity, Culture and Health (CRECH). He is a faculty member at the Center of Social Epidemiology and Population Health (CSEPH). He is also affiliated with the University of Michigan Transportation Research Institute (UMTRI). He received his PhD in Statistics from Harvard University in 1987. Before joining the University of Michigan in 1994, he was on the faculty in the Department of Biostatistics at the University of Washington. He continues to be involved in several projects at the Cardiovascular Health Research Unit (CHRU) at the University of Washington. His 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. He has developed a SAS based software for imputing the missing values for a complex data set and can be downloaded from www.iveware.org.
American Statistical Association
- PhD, Statistics, Harvard University, 1987
- MS, Statistics, Miami University, 1983
- MSc, Statistics, Nagpur University, 1979
- BSc, Nagpur University, 1977
My 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 do have 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. I have developed SAS based software, IVEware, for performing multiple imputation analysis and the analysis of complex survey data.
My 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. My applied interests include cardiovascular epidemiology, social epidemiology, health disparity, health care utilization, and social and economic sciences.
I also have an appointment in the Survey Methodology Program at the Institute for Social Research. The program, 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.
Siddique, J., Daniels, M. J., Carroll, R. J., Raghunathan, T. E., Stuart, E. A. and Freedman, L. S. (2019), Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study. Biometrics. Accepted Author Manuscript. doi:10.1111/biom.13044
Cutler, D., Ghosh, K., Messer, K., Raghunathan, T., Stewart, S., Rosen, A. (2019). Explaining The Slowdown In Medical Spending Growth Among The Elderly, 1999-2012 Health Affairs 38(2): 222-229.
Yucel, R.M., Zhao, E., Schenker, N., Raghunathan TE.; Sequential Hierarchical Regression Imputation, Journal of Survey Statistics and Methodology, Volume 6, Issue 1, 1 March 2018, Pages 1-22. https://doi.org/10.1093/jssam/smx004
Lohr, Sharon L.; Raghunathan, Trivellore E. Combining Survey Data with Other Data Sources. Statist. Sci. 32 (2017), no. 2, 293--312. DOI:10.1214/16-STS584. http://projecteuclid.org/euclid.ss/1494489817.
Zhou, H., Elliott, M., Raghunathan, T. E. Synthetic Multiple-Imputation Procedure for Multistage Complex Samples. Journal of Official Statistics, 2016:32(1), pp. 231-256. Retrieved 17 May. 2016, PMCID: PMC5542708 DOI:10.1515/jos-2016-0011
Zhou, H., Elliott, M., Raghunathan, T. E. Multiple Imputation in Two-stage Cluster Samples Using the Weighted Finite Population Bayesian Bootstrap. Journal of Survey Statistics and methodology, 2016:Jan 31, PMCID: PMC5719896 DOI: 10.1093/jssam/smv031
Bondarenko, I., Raghunathan, T. E. Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models. Statistics in Medicine, 2016: DOI: 10.1002/sim.6926
Zhu, J., and Raghunathan, T. E. Convergence properties of a sequential regression multiple imputation algorithm. Journal of the American Statistical Association, 2015:110(511), 1112-1124. DOI:10.1080/01621459.2014.948117
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Areas of Expertise: Biostatistics