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Sander Greenland

Professor Emeritus


DepartmentsType of Faculty
Contact Information

UCLA School of Public Health
Department of Epidemiology
Box 951772. 71-279A CHS
Los Angeles, CA 90095

Areas of Interest: 

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

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 400 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.


AB, Mathematics, University of California, Berkeley
MA, Mathematics, University of California, Berkeley
MS, Public Health, University California, Los Angeles
DrPH, Epidemiology, University of California, Los Angeles
Selected Publications: 
  1. Greenland S. Multiple-bias modeling for analysis of observational data. J Royal Stat Soc A 2005; 168; 267-308.
  2. Greenland S. Bayesian perspectives for epidemiologic research, part I. Int J Epidemiol 2006; 35: 765-78.
  3. Greenland S, Gustafson P. Adjustment for independent nondifferential misclassification does not increase certainty that an observed association is in the correct direction. Am J Epidemiol 2006; 164: 63-8.
  4. Greenland S. Smoothing observational data: a philosophy and implementation for the health sciences. Int Statist Rev 2006; 74: 31-46.
  5. Greenland S. Bayesian perspectives for epidemiologic research, part II. Int J Epidemiol 2007; 36: 195-202.
  6. Greenland S. Prior data for non-normal priors. Stat Med 2007; 26: 3578-90.
  7. Greenland S. Maximum-likelihood and closed-form estimators of epidemiologic measures under misclassification. J Statist Planning Inference2007; 138: 528-38.
  8. Greenland S. Variable selection and shrinkage in the control of confounders. Am J Epidemiol 2008; 167: 523-9.
  9. Greenland S, Kheifets L. Designs and analyses for exploring the relation of magnetic fields to childhood leukemia. Scand J Public Health 2009; 37: 83-92.
  10. Greenland S. Interactions in epidemiology: relevance, identification, estimation. Epidemiology 2009; 20: 14-7.
  11. Greenland S. Dealing with uncertainty about investigator bias. J Epid Community Health 2009;63: 593-8.
  12. Greenland S. Weaknesses of Bayesian model averaging for meta-analysis in the study of vitamin E and mortality. Clin Trials 2009; 6:42-6.
  13. Greenland S. Bayesian perspectives for epidemiologic research, part III. Int J Epidemiol2009; 38: 1662-73.
  14. Greenland S. Relaxation penalties and priors for plausible modeling of nonidentified bias sources. Stat Science 2009; 24: 195-210.
  15. Greenland S. Simpson’s paradox from adding constants in contingency tables as an example of Bayesian noncollapsibility. The American Statistician 2010; 64:340-4.
  16. Greenland S and Poole C. Problems in common interpretations of statistics in scientific articles, expert reports, and testimony. Jurimetrics 2011; 51: 113-29
  17. Greenland S and Pearl J. Adjustments and their consequences – collapsibility analysis using graphical models. Int Statist Review 2011; 79: 401-26.
  18. Greenland S. Null misinterpretation in statistical testing and its impact on health risk assessment. Prev Med 2011; 53: 225-8.
  19. Greenland S. Cornfield, risk relativism, and research synthesis. Stat Med 2012; 31: 2773-7.
  20. Greenland S. Nonsignificance plus high power does not imply support for the null over the alternative. Ann Epidemiol 2012; 22: 364–8.
  21. Greenland S, Poole C. Living with P values. Epidemiology 2013; 24: 62-8.
  22. Greenland S, Poole C. Living with statistics in observational research. Epidemiology 2013; 24: 73-8.
  23. Greenland S, Pearce N. Statistical foundations for model-based adjustments. Ann Rev Public Health 2015; 36: 89-108.
  24. Greenland S. Concepts and pitfalls in measuring and interpreting causal attribution, preventive potential, and causation probabilities. Ann Epidemiol 2015; 25: 155-161.
  25. Greenland S, Mansournia M. Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions.Stat Med 2015; 34: 3133–3143.
  26. Greenland S, Senn SJ, Rothman KJ, Carlin JC, Poole C, Goodman SN, Altman DG. Statistical tests, confidence intervals, and power: A guide to misinterpretations. Eur J Epidemiol 31, 337-350.
  27. Greenland S, Mansournia M, Altman DG. Sparse-data bias: A problem hiding in plain sight. Br Med J 2016; 353:i1981, 1-6.
  28. Greenland S, Daniel R, Pearce N. Outcome modeling strategies in epidemiology: traditional methods and basic alternatives. Int J Epidemiol 2016; 45: 565–575.
  29. Greenland S. For and against methodology: Some perspectives on recent causal and statistical inference debates. Eur J Epidemiol 2017; 32; 3-20.
  30. Greenland S. The need for cognitive science in methodology. Am J Epidemiol 2017: 186; 639-645
  31. Greenland S, Hofman A. Multiple comparisons controversies are about context and costs, not frequentism vs. Bayesianism. European Journal of Epidemiology 2019; 34(9); 801-808.
  32. Greenland S. Some misleading criticisms of P-values and their resolution with S-values. The American Statistician 2019, 73, supplement 1, 106-114.
  33. Greenland S, Fay MP, Brittain EH, Shih JH, Follmann DA, Gabriel EE, Robins JM. On causal inferences for personalized medicine: how hidden causal assumptions led to erroneous causal claims about the D-value. The American Statistician, 2020; 74; 243-248.
  34. Greenland S. An argument against E-values for assessing the plausibility that an association would be explained away by residual confounding. International Journal of Epidemiology 2020; 49; 1501-1503.
  35. Greenland S. Analysis goals, error-cost sensitivity, and analysis hacking: essential considerations in hypothesis testing and multiple comparisons. Pediatric and Perinatal Epidemiology 2020; 35; 8-23.
  36. Greenland S. Dealing with the inevitable deficiencies of bias analysis – and all analyses. American Journal of Epidemiology 2021; 190; in press.


  1. Greenland S (ed.) (1987). Evolution of Epidemiologic Ideas: Annotated Readings on Concepts and Methods. Chestnut Hill, MA: Epidemiology Resources Inc.
  2. Rothman KJ, Greenland S (1998). Modern Epidemiology, 2nd ed. Philadelphia: Lippincott-Raven.
  3. Porta MS, Greenland S, Last JM (eds). (2008). A Dictionary of Epidemiology, 5th ed. New York: Oxford University Press.
  4. Rothman KJ, Greenland S, Lash TL (2008). Modern Epidemiology, 3rd ed. Philadelphia: Lippincott-Wolters-Kluwer.
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