Faculty Profile
Michael R. Elliott, PhD
- Professor, Biostatistics
- Research Professor, Survey Methodology
Michael Elliott is a Professor of Biostatistics at the University of Michigan School
of Public Health and Research Professor at the Institute for Social Research. He received
his PhD 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. Dr. Elliott has served as Editor for the Journal of the Royal
Statistical Society, Series A since 2023 and as Editor of the Journal of Survey Statistics
and Methodology from 2018-2021. He was Associate Chair of Academic Affairs for the
Department of Biostatistics from 2018-2021.
Dr. Elliott has over 275 refereed publications; his statistical research focuses around
the broad topic of ""missing data,"" including the design and analysis of sample surveys,
causal and counterfactual inference, and latent variable models. He has worked closely
with collaborators in injury research, pediatrics, women's health, the social determinants
of physical and mental health, and smoking cessation and cannabis use research.
Elliott is a Fellow of the American Statistical Association and an Elected Member
of the International Statistical Institute. He received the 2010 Gertrude M. Cox
Award from the Washington D.C. Statistical Society, and the 2024 Monroe G. Sirken
Award in Interdisciplinary Survey Methods Research, given jointly by the American
Statistical Association and the American Association of Public Opinion Research.
- PhD, University of Michigan, 1999
- MS, University of Michigan, 1997
- BA, University of Chicago, 1985
- Survey design and analysis, missing data, causal inference, longitudinal data
- Developing model-based Bayesian approaches that complement traditional design-based
analyses of complex sample survey data.
- Combining probability and non-probability samples.
- Developing adaptive and responsive sampling designs for surveys.
- Assessing and accounting for interview and mode effects in multi-mode surveys.
- Developing methods to assess surrogate markers using causal inference (determining
to what degree easy-to-observe biomarkers are on causal pathway for outcomes that
are expensive or time-consuming to observe).
- Developing methods to adjust for bias due to ""treatment by indication"" in observational
settings, where preliminary outcomes may drive treatment decisions.
- Improving the generalizability of clinical trials by incorporating information from
relevant probability samples.
- Developing models that focus on variability structures rather than, or in addition
to, mean structures to predict health outcomes.
- Developing models that accommodate measure error in estimation.
Research Projects:
"Lead biostatistician on the Michigan cohort for the Environmental Influences on Child
Health Outcomes (ECHO), a national study of children for the National Institutes of
Health.
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.
I collaborate with researchers at the Institute for Social Research on environmental
effects on health outcomes, including factors such as work stress and built environment
on health in later life.
I collaborate with researchers at the University of Michigan Transportation Research
Institute and at Children's Hospital of Philadelphia on a wide variety of topics related
to driving behavior and driver safety, including work on a large naturalistic driving
study funded though the Michigan Institute for Data Science (MIDAS) and a large randomized
trial of driver training of novice drivers in Pennsylvania.
I collaborate with smoking cessation and cannabis use researchers, in particular to
assess the impact of various changes in the legal and regulatory environment restrictions
on use of tobacco and cannabis.
I collaborate with researchers at the University of Michigan Transportation Research Institute on a wide variety of topics related to driving behavior and driver safety, including work on a large naturalistic driving study funded though the Michigan Institute for Data Science (MIDAS).
I collaborate with smoking cessation researchers, in particular to assess the impact of various legal restrictions on smoking behavior.
Elliott, M.R., Kerver, J.M., Drew, A., Watson, K., Kornatowski, B., Norman, G.S.,
Copeland, G.E., Leissou, E., Ridenour, T., Kruger-Ndiaye, S., Ma, T., Ruden, D., Barone,
C.B., Keating, D.P., Sokol, R.J., Johnson, C.C. Paneth, N. (2025). “Obtaining a Probability
Sample of a Pregnancy Cohort of Births: A Review of the Problem and a Practical Solution,”
to appear in American Journal of Epidemiology.
Chen, I., Wu, Z., Harlow, S.D., Karvonen-Guitierrez, Hood, M.M., Elliott, M.R. (2024).
“Variance as a Predictor of Health Outcomes: Using Subject-Level Trajectories and
Variability of Sex Hormones to Predict Body Fat Changes in Peri- And Post-Menopausal
Women,” Annals of Applied Statistics, 18, 1642-1667.
Coffey, S.M., Elliott, M.R. (2024). “Optimizing Data Collection Interventions to Balance
Cost and Quality in a Sequential Multimode Survey,” Journal of Survey Statistics and
Methodology, 12, 741-763.
Elliott, M.R., Carroll, O., Grieve, R., Carpenter, J. (2023). “Improving Transportability
of Randomized Controlled Trial Inference Using Robust Prediction Methods,” Statistical
Methods in Medical Research, 32, 2365-2385.
Rafei., A., Flannagan, C.A.C., West,. B.T., Elliott, M.R. (2022). ""Robust Bayesian
Inference for Big Data: Combining Sensor-based Records with Traditional Survey Data,""
Annals of Applied Statistics, 16, 1038-1070.
Zhou, T., Elliott, M.R., Little, R.J.A. (2019). ""Penalized Spline of Propensity Methods
for Treatment Comparison,"" Journal of the American Statistical Association (with
discussion), 114, 1-38.
Elliott, M.R., Valliant, R. (2017). Inference for Non-probability Samples. Statistical
Science, 32, 249-264.
Zhou, H., Elliott, M.R., Raghunathan, T.E. (2016). A Two-Step Semiparametric Method
to Accommodate Sampling Weights in Multiple Imputation. Biometrics 72, 242-252.
Elliott, M.R., Conlon, A.S.C., Li, Y., Kaciroti, N., Taylor, J.M.G. (2015). Surrogacy
Marker Paradox Measures in Meta-Analytic Settings. Biostatistics, 16, 400-12.
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.
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., 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.
M4124 SPHII
1415 Washington Heights
Ann Arbor, MI 48109
Email: [email protected]
Office: 734-647-5160
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