Elizabeth Stuart, PhD, uses statistical methods to help learn about the effects of public health programs and policies, often with a focus on mental health and substance use.
Mental health; policy evaluation; biostatistics; causal inference; generalizability of results from randomized trials to target populations; external validity; non-experimental study; propensity scores
Experiences & Accomplishments
Trained as a statistician, my primary research interests are in the development and use of methodology to better design and analyze the causal effects of public health and educational interventions. In this way I hope to bridge statistical advances and research practice, working with mental health and educational researchers to identify and solve methodological challenges. I am particularly interested in the trade-offs in different designs for estimating causal effects, especially in terms of improving internal validity of non-experimental studies and external validity of randomized studies. This translates into two primary research areas. First, one of my primary research areas is in the use of propensity score methods for estimating causal effects in non-experimental studies (essentially as a tool to improve internal validity and reduce confounding). My interests in this area include providing advice for researchers in terms of best practice for estimation, diagnostics, and use of propensity score methods. This also includes investigation of how to handle complexities in propensity score methods, including multilevel data settings, covariate measurement error, and complex survey data. My second primary research area is in methods to assess and enhance the external validity (generalizability) of randomized trial results and enable policymakers to determine how applicable the results of a particular randomized study are to their own target population. I also have interests in handling complexities in randomized experiments, in particular missing data and non-compliance. The applied areas I focus on include autism, the long-term consequences of adolescent substance abuse, education, mental health services and systems, and the effects of health care reform models on mental health service use.
Honors & Awards
Myrto Lefkopoulou Award, Harvard Univ. Dept. of Biostatistics (2018) Gertrude Cox Award, Washington Statistical Society/American Statistical Association (2017) Health Policy Statistics Section mid-career award, American Statistical Association (2015) Fellow, American Statistical Association (2014) JHSPH AMTRA Advising, Mentoring, and Teaching Recognition Award (2010, 2015) JHSPH Golden Apple Award for Excellence in Teaching (2010) Warren Miller Prize for best paper published in Volume 15 of Political Analysis, (Paper also selected as a “Fast Breaking Paper” by Thomson Reuters (2008) National Science Foundation Graduate Research Fellowship (1999-2002) Student paper award, American Statistical Association (2001) Gertrude Cox award, American Statistical Association (2000) William Cochran award in Statistics, Harvard University (1999) Pokora Prize for Mathematics, Smith College (1997) Phi Beta Kappa, Smith College (1997) Magna cum laude, Smith College (1997) Barry M. Goldwater Scholar (1995-1997) Robert C. Byrd Scholar (1993-1997)
Selected publications highlighting methodological interests.
- Erlangsen, A., …, Stuart, E.A., et al. (2014). Short and long term effects of psychosocial therapy provided to persons after deliberate self-harm: a register-based, nationwide multicentre study using propensity score matching. Lancet Psychiatry. Published online November 24, 2014. Open access link: http://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(14)00083-2/fulltext
- Stuart, E.A., Bradshaw, C.P., and Leaf, P.J. (2015). Assessing the generalizability of randomized trial results to target populations. Prevention Science 16(3): 475-485.
- Stuart, E.A., Cole, S.R., Bradshaw, C.P., and Leaf, P.J. (2011). The use of propensity scores to assess the generalizability of results from randomized trials. The Journal of the Royal Statistical Society, Series A 174(2): 369-386. PMCID: 4051511. http://www.ncbi.nlm.nih.gov/pubmed/24926156.
- Stuart, E.A. (2010). Matching Methods for Causal Inference: A review and a look forward. Statistical Science 25(1): 1-21. PMCID: PMC2943670. http://www.ncbi.nlm.nih.gov/pubmed/20871802.
- Imai, K., King, G., and Stuart, E.A. (2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society, Series A 171: 481-502. http://onlinelibrary.wiley.com/doi/10.1111/j.1467-985X.2007.00527.x/full.
Infant Safe Sleep Intervention Trial
Qualitative evaluation of the implementation dimension of the Pennsylvania Hospitals Opioid Learning Action Network