Sander Greenland

Sander Greenland is Emeritus Professor of Epidemiology and Statistics at UCLA. He received honors Bachelor's and Master's degrees in Mathematics from the University of California Berkeley where he was Regent's and National Science Foundation Fellow in Mathematics, followed by Master's and Doctoral degrees in Epidemiology from UCLA where he was Regent's Fellow in Epidemiology. He became Professor of Epidemiology in the UCLA Fielding School of Public Health in 1989 and Professor of Statistics in the UCLA College of Letters and Science in 1999. He was made a Fellow of the Royal Statistical Society in 1993, a Fellow of the American Statistical Association in 1998, and was given an honorary doctorate by Aarhus University in 2013. He has published over 450 scientific papers and book chapters, and co-authored a leading advanced textbook on epidemiology. His many contributions to statistics and epidemiology include causal inference, bias analysis, and meta-analysis methods, with a focus on extensions, limitations, and misuses of statistics in nonexperimental studies, especially in postmarketing surveillance of drugs, vaccines, and medical devices. He has served on the editorial boards of many statistics and epidemiology journals, as an advisor for the World Health Organization, the U.S. Food and Drug Administration, the Environmental Protection Agency, the Centers for Disease Control, and the National Academy of Sciences, and has been an invited speaker at universities and conferences throughout the world. 

View CV


  • DrPH, Epidemiology, University of California, Los Angeles, CA
  • MS, Public Health, University California, Los Angeles, CA
  • MA, Mathematics, University of California, Berkeley, CA
  • BA, Mathematics, University of California, Berkeley, CA

Areas of Interest

Epidemiologic methodology; statistical methods for epidemiologic data; epidemiologic assessment of medicines and medical technology; foundations of nonexperimental inference. 


  • Greenland S (ed.) (1987). Evolution of Epidemiologic Ideas: Annotated Readings on Concepts and Methods. Chestnut Hill, MA: Epidemiology Resources Inc. 

  • Rothman KJ, Greenland S (1998). Modern Epidemiology, 2nd ed. Philadelphia: Lippincott-Raven. 

  • Porta MS, Greenland S, Last JM (eds). (2008). A Dictionary of Epidemiology, 5th ed. New York: Oxford University Press. 

  • Rothman KJ, Greenland S, Lash TL (2008). Modern Epidemiology, 3rd ed. Philadelphia: Lippincott-Wolters-Kluwer. 

Selected Publications, 2004 Onward

  • Greenland, S. (2004). An overview of methods for causal inference from observational studies. Chapter 1 in: Gelman, A. and Meng, X.L. (eds.). Applied Bayesian Modeling and Causal Inference from an Incomplete-Data Perspective. New York: Wiley, 3-13. 

  • Greenland, S., Gago-Dominguez, M., and Castellao, J.E. (2004). The value of risk-factor ("black-box") epidemiology (with discussion). Epidemiology, 15, 519-535.

  • Greenland, S. (2004). The need for critical appraisal of expert witnesses in epidemiology and statistics. Wake Forest Law Review, 39, 291-310.

  • Greenland S. Multiple-bias modeling for analysis of observational data (with discussion). Journal of the Royal Statistical Society series A, 2005, 168, 267-308. 

  • Greenland S. Bayesian perspectives for epidemiologic research, part I. International Journal of Epidemiology 2006; 35: 765-78. 

  • Greenland S. Bayesian perspectives for epidemiologic research, part II. International Journal of Epidemiology 2007; 36: 195-202. 

  • Greenland S. Prior data for non-normal priors. Statistics in Medicine 2007; 26: 3578-90. 

  • Greenland S. Variable selection and shrinkage in the control of confounders. American Journal of Epidemiology 2008; 167: 523-9. 

  • Greenland S. Dealing with uncertainty about investigator bias. Journal of Epidemiology and Community Health 2009;63: 593-8. 

  • Greenland S. Weaknesses of Bayesian model averaging for meta-analysis in the study of vitamin E and mortality. Clinical Trials 2009; 6:42-6. 

  • Greenland S. Bayesian perspectives for epidemiologic research, part III. International Journal of Epidemiology 2009; 38: 1662-73. 

  • Greenland S. Relaxation penalties and priors for plausible modeling of nonidentified bias sources. Statistical Science 2009; 24: 195-210. 

  • Greenland S. Simpson’s paradox from adding constants in contingency tables as an example of Bayesian noncollapsibility. The American Statistician 2010; 64:340-4. 

  • Greenland, S., Pearl, J. (2011). Adjustments and their consequences – collapsibility analysis using graphical models. International Statistical Review, 79 (3), 401-426.

  • Greenland, S. (2011). Null misinterpretation in statistical testing and its impact on health risk assessment. Preventive Medicine, 53, 225-228.

  • Greenland, S. (2012). Transparency and disclosure, neutrality and balance: shared values or just shared words? Journal of Epidemiology and Community Health, 66, 967–970,

  • Greenland, S. (2012). Nonsignificance plus high power does not imply support for the null over the alternative. Annals of Epidemiology, 22, 364–368.

  • Greenland, S., and Poole, C. (2013). Living with statistics in observational research. Epidemiology, 24, 73-78.

  • Greenland, S., and Mansournia, M.A. (2015). Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness. European Journal of Epidemiology, 30, 1101-1110.

  • Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.C., Poole, C., Goodman, S.N., Altman, D.G. (2016). Statistical tests, confidence intervals, and power: A guide to misinterpretations. The American Statistician, 70, online supplement 1 at,

  • Greenland, S. (2017). For and against methodology: Some perspectives on recent causal and statistical inference debates. European Journal of Epidemiology, 32, 3-20,

  • Greenland, S. (2017). The need for cognitive science in methodology. American Journal of Epidemiology, 186, 639-645,

  • Greenland, S. (2019). Some misleading criticisms of P-values and their resolution with S-values. The American Statistician, 73, supplement 1, 106-114, open access at

  • Greenland, S., Fay, M.P., Brittain, E.H., Shih, J.H., Follmann, D.A., Gabriel, E.E., Robins, J.M. (2020). On causal inferences for personalized medicine: how hidden causal assumptions led to erroneous causal claims about the D-value. The American Statistician, 74, 243-248,, open access version at

  • Greenland, S. (2021). Analysis goals, error-cost sensitivity, and analysis hacking: essential considerations in hypothesis testing and multiple comparisons. Pediatric and Perinatal Epidemiology, 35, 8-23.

  • Greenland, S. (2021). Dealing with the inevitable deficiencies of bias analysis – and all analyses. American Journal of Epidemiology, 190, 1617-1621.

  • Greenland, S., Mansournia, M., and Joffe, M. (2022). To curb research misreporting, replace significance and confidence by compatibility. Preventive Medicine, 164,

  • Greenland, S. (2022). The causal foundations of applied probability and statistics. Ch. 31 in: Dechter, R., Halpern, J., and Geffner, H., eds. Probabilistic and Causal Inference: The Works of Judea Pearl. ACM Books, no. 36, 605-624,, corrected version at

  • Greenland, S. (2023). Connecting simple and precise p-values to complex and ambiguous realities. Scandinavian Journal of Statistics, 50, in press,,

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