Michael Elliott is Professor of Biostatistics at the University of Michigan School of Public Health and Research Scientist at the Institute for Social Research. He received his Ph.D. in biostatistics in 1999 from the University of Michigan. Prior to joining the University of Michigan in 2005, he held an appointment as an Assistant Professor at the Department of Biostatistics and Epidemiology at the University of Pennsylvania School of Medicine, and prior to that as a Visiting Professor of Biostatistics at the University of Michigan School of Public Health and as a Visiting Research Scientist at the University of Michigan Transportation Research Institute. Dr. Elliott's statistical research interests focus around the broad topic of "missing data," including the design and analysis of sample surveys, casual and counterfactual inference, and latent variable models. He has worked closely with collaborators in injury research, pediatrics, women's health, and the social determinants of physical and mental health. Dr. Elliott chairs the BRFSS Survey Oversight committee, organized by the American Statistical Association at the request of the Centers for Disease Control, and serves as an Associate Editor for the Journal of the American Statistical Association and the Journal of the Royal Statistical Society C: Applied Statistics.
BIOSTAT855: Regression Models in Complex Sample Design Settings (JPSM/MPSM 895)
BIOSTAT870: Analysis of Repeated Measurements
Ph.D., Biostatistics, University of Michigan, 1999
M.S., Biostatistics, University of Michigan, 1997
B.A., Mathematics, University of Chicago, 1985
Research Interests & Projects
My methodological research focuses in two major areas: design and analysis of population-based surveys, and development of causal modeling estimators. Together, these areas may be coherently thought of as special cases of missing data: population surveys are censuses from which typically most of the population data is missing, while causal estimators, in particular "potential outcomes" models, can be viewed as analyses in which outcomes under different treatment assignments cannot, by design, be observed.
My focus in the design and analysis of population-based surveys has been on the development of model-based Bayesian approaches that complement traditional design-based analyses of complex sample survey data. Traditional design-based approaches to analyzing survey data are non-parametric, but rely on asymptotic assumptions and can be highly inefficient. Model-based approaches can have better small sample and efficiency properties, but often lack the robustness of design-based methods. Modern techniques such as non-parametric regression and Dirichlet processes allow development of models than can balance robustness-efficiency tradeoffs. In the causal modeling arena, I have considered links between the Rubin Causal Model (RCM), which posits "principal strata" formed by pre-randomization counterfactual compliance behavior, and the marginal structural mean model proposed by Robins and others, as well as explored extensions of the RCM to longitudinal randomized trials where patients have failed to adhere to their randomization arm. I have also used the principal strata concept in the context of developmental toxicology, where standard methods fail to account for the effect of dose on survival, with attendant biases on dose-response estimates for developmental outcomes such as birth weight in animal models. Beyond these two areas, I have also considered other latent variable methodology problems, developing extensions of generalized growth curve mixture models in the context of psychiatric affect data. Most recently I have started to consider whether models that focus on variability structures rather than, or in addition to, mean structures, might be useful in this or other analytic settings (e.g., hormone and menstruation time series data.)
I have a wide variety of applied interests. I have worked a great deal on pediatric issues, including serving as the lead biostatistician on the Partners for Child Passenger Safety, which has conducted research into the cause and prevention of injuries to children in passenger vehicles, and I am serving in a similar capacity on the Youthful Driver Initiative, which is attempting to design more effective drivers education efforts via training as well as family and community involvement. I have worked in women's health issues, including studies designed to understand the onset of menopause and to predict and ultimately treat health problems that accompany the menopausal transition. More recently I have become involved in the estimation of contextual neighborhood effects on health outcomes, in the context of collaborations with researchers involved with the American's Changing Lives (ACL) study -- a four-wave longitudinal study of health and labor status over a 15-year period (1986-2001) in a random sample of adults 25 and older at the start of the study -- and the Chicago Mind-Body (CMB) study -- cross-sectional survey of a random sample of adults in Chicago in 2001-2003.
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Chen, Q., Elliott, M.R., Little, R.J.A (2012). Bayesian Inference for Finite Population Quantiles from Unequal Probability Samples Survey Methodology, 38 , 203-214.
Elliott, M.R., Sammel, M.D., Faul, J. (2012). Variability as a Predictor of Health in Longitudinal Studies. Statistics in Medicine, 31, 2745-2756.
Li Y., Taylor, J.M.G., Elliott, M.R., Sargent, D.J. (2011). Causal Assessment of Surrogacy in a Meta Analysis of Colorectal Cancer Trials. Biostatistics, 12, 478-492.
Elliott, M.R., Raghunathan, T.E., Li, Y. (2010). Bayesian Inference for Causal Mediation Effects Using Principal Stratification with Dichotomous Mediators and Outcomes. Biostatistics, 11, 353-372.
Lin, J.Y., Ten Have T.R., Elliott, M.R. (2009). Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance. Biometrics, 65, 505-513.
Elliott, M.R. (2009). Model Averaging Methods for Weight Trimming in Generalized Linear Regression Models. Journal of Official Statistics, 25, 1-20.
Elliott, M.R. (2007). Bayesian Weight Trimming for Generalized Linear Regression Models. Survey Methodology, 33, 23-34.
Elliott, M.R., Little, R.J.A. (2005). A Bayesian Approach to 2000 Census Evaluation using A.C.E. Survey Data and Demographic Analysis. Journal of the American Statistical Association, 100, 380-388.
Elliott, M.R. and Davis, W.W. (2005). Obtaining Cancer Risk Factor Prevalence Estimates in Small Areas: Combining Data from the Behavioral Risk Factor Surveillance Survey and the National Health Interview Survey. Applied Statistics, 54, 595-609.
Ten Have, T.R., Elliott, M.R., Joffe, M., Zanutto, E. (2004). Causal Linear Models for Non-Compliance under Randomized Treatment with Univariate Continuous Response. Journal of the American Statistical Association, 99, 16-25.
International Statistical Institute
American Statistical Association
Royal Statistical Society
ENAR, International Biometric Society
Association for the Advancement of Automotive Medicine