Other

Selected Papers

  • Nguyen, T. Q., Stuart, E. A., Scharfstein, D. O., & Ogburn, E. L. (2024). Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects. Statistics in Medicine 43(19): 3664-3688. Article link

  • Knapp, E., Stuart, E.A., Wilson, R., Zhang, A., Tseng, E., Cheskin, L., Bass, E., Kharrazi, H., and Bennett, W. (2019). Methods and risks of bias in natural experiments in obesity: Opportunities for the future informed by a systematic review. Obesity. 27(12):1950-1957. doi: 10.1002/oby.22645. Pubmed Link

  • Li, P., and Stuart, E.A. (2019). Best (but oft-forgotten) practices: Missing data methods in randomized controlled nutrition trials. The American Journal of Clinical Nutrition nqy271. Published online 22 February 2019. Article link

  • Callahan, K., and Stuart, E.A. (2018). Bringing Evidence to Bear on Public Health in the United States. Public Health Reports 133(1): Supplement 20S-24S. Article link

  • Mercer, A., Kreuter, F., Keeter, S., and Stuart, E.A. (2017). Theory and Practice in Non- Probability Surveys: Parallels Between Causal Inference and Survey Inference. Public Opinion Quarterly 81: 250-279. (With invited discussion and rejoinder). Article link

  • Westlund, E., and Stuart, E.A. (2017). The Nonuse, Misuse, and Proper Use of Pilot Studies in Experimental Evaluation Research. American Journal of Evaluation 38(2): 246-261. Article link

  • Westreich, D., Edwards, J.K., Rogawski, E., Hudgens, M.G., Stuart, E.A., and Cole, S.R. (2016). Causal impact: Epidemiological approaches for a public health of consequence. American Journal of Public Health 106(6): 1011-1012. PMCID: PMC4880276. Article link

  • George, B.J., Beasley, T.M., Brown, A.W., Dawson, J., Dimova, R., Divers, J., Goldsby, T.U., Heo, M., Kaiser, K.A., Keith, S.W., Kim, M.Y., Li, P., Mehta, T., Oakes, J.M., Skinner, A., Stuart, E.A., and Allison, D.B. (2016). Common Scientific and Statistical Errors in Obesity Research. Obesity 24: 781-790. PMCID: PMC4817356. Article link

  • Li, P., Stuart, E.A, and Allison, D.B. (2015). Multiple imputation: A flexible tool for handling missing data. Journal of the American Medical Association 314(18): 1966-1967. NIHMS ID: 731701. PMCID: PMC4638176. Article link

Last updated on Aug 5, 2024