Sander Greenland

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桑德·格陵兰
文件:SG NYC c.png
Born1951年1月16日（72岁）
Nationality美国
Alma mater加州大学洛杉矶分校
加州大学伯克利分校
Scientific career
Fields统计学
流行病学
Institutions加州大学洛杉矶分校
Doctoral advisor雷蒙德·纽佐尔

Sander Greenland (born January 16, 1951) is an American statistician and epidemiologist with many contributions to statistical and epidemiologic methods including Bayesian and causal inference, bias analysis, and meta-analysis. His focus has been the extensions, limitations, and misuses of statistical methods in nonexperimental studies, especially in postmarketing surveillance of drugs, vaccines, and medical devices. 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, and then received Master's and Doctoral degrees in Epidemiology from the University of California, Los Angeles (UCLA), where he was Regent's Fellow in Epidemiology. After serving as an Assistant Professor of Biostatistics at Harvard, he joined the UCLA Epidemiology faculty in 1980 where he became Professor of Epidemiology in the Fielding School of Public Health in 1989, and Professor of Statistics in the UCLA College of Letters and Science in 1999. He moved to Emeritus status in 2012 and the following year he was awarded an honorary Doctor of Medicine by the University of Aarhus, Denmark.

【终译】桑德 格陵兰（出生于1951年1月16日）是美国统计学家及流行病学家，对统计学与流行病学方面做出了诸多贡献，如贝叶斯与因果推断、偏差分析和元分析。他的研究聚焦于非实验性研究中统计方法的拓展、局限和滥用，尤其是在药品、疫苗和医疗设备的售后监测等环节。联系邮箱为[[1]]。他在加州大学伯克利分校获得数学学士和硕士学位，并成为该校数学系的评议员及国家科学基金会数学研究员，之后更是获得了加州大学洛杉矶分校的流行病学硕士和博士学位，并成为流行病学系的评议员。在结束哈佛大学生物统计系助理教授的任期后，他于1980年加入加州大学洛杉矶分校的流行病学系，并在1989年成为菲尔丁公共卫生学院流行病学教授，在1999年成为加州大学洛杉矶分校文理学院的统计学教授。2012年，转为名誉教授，次年被授予丹麦奥胡斯大学荣誉医学博士称号。

Dr. Greenland has published over 400 scientific papers and book chapters, over a dozen of which have been cited over a thousand times and several over two thousand times, including[1][2] and one of which was chosen as a discussion paper by the Royal Statistical Society.[3] He is the co-author of a leading advanced textbook on epidemiology (currently in its 3rd edition[4]). He was made a Fellow of the Royal Statistical Society in 1993 and a Fellow of the American Statistical Association in 1998,[5] and has received numerous teaching and service awards. He has been an invited lecturer at over 200 scientific institutions worldwide including Harvard, Oxford, Cambridge, Columbia, Stanford, Yale, and Erasmus universities, the Massachusetts Institute of Technology, the National Institutes of Health, the Santa Fe Institute, and the Karolinska Institute in Sweden. He has also served as a consultant to U.S. governmental agencies including the National Academy of Sciences, the Food and Drug Administration, the Centers for Disease Control, and the Environmental Protection Agency, as well the World Health Organization. He has further served as an editor for statistical and epidemiologic journals and books including the Dictionary of Epidemiology sponsored by the International Epidemiological Association.[6]

【终译】桑德 格陵兰发表了400多篇学术论文及著作，有十几篇学术论文被引用一千多次[1]，有几篇更是被引用多达两千多次[2]，其中一篇被英国皇家统计学会列为讨论文件[3]。他是一本流行病学高级教科书（目前已出版第三版[4]）的合著者。他于1993年成为英国皇家统计学会会员，1998年成为美国统计协会会员[5]，并获得多项教学和服务奖。他曾应邀在全球200多所科研机构担任讲师，包括哈佛大学、牛津大学、剑桥大学、哥伦比亚大学、斯坦福大学、耶鲁大学、伊拉斯姆斯大学、麻省理工学院、美国国立卫生研究院、圣菲研究所和瑞典卡罗琳学院。他还担任美国政府机构的顾问，包括美国国家科学院、食品和药物管理局、疾病控制中心、环境保护局以及世界卫生组织。他还担任统计和流行病学期刊和书籍的编辑，包括国际流行病学协会主办的《流行病学词典》[6]，并曾应邀在世界各地的大学和会议上发表演讲。

【补充】桑德 格陵兰也对统计学和流行病学领域做出了许多贡献：包括因果推断、偏见分析和元分析方法，重点是在非实验性研究中，特别是在药品、疫苗和医疗设备的上市后监测中，统计学的扩展、局限和误用。

He is a leading critic of arbitrary significance thresholds in science[7][8][9][10] and has drawn attention to misunderstandings of p-values.[11]

【终译】他是统计科学中随机显著性阈值的一名主要评论家[7][8][9][10]，他阐述了对关于p值误导性的一些批评，并利用S值解决了相关问题。[11]

【补充】近些年发表了多篇关于假设检验及多重比较的论文，引起了学界对此的注意。并且还曾发表通过残余的混淆因子反驳E值对评估关联效应可信度的论文，所谓E值是指，在控制已测量混淆因素的情况下，使未测量的混杂效应完全抹去暴露对结果的关联效应的最小值。其中在2021年在美国传染病杂志上发表论文，旨在解决实验数据误差分析中不可避免地缺陷，在传染病领域做出了杰出的贡献。

教育经历

  • AB, Mathematics, University of California, Berkeley
  • MA, Mathematics, University of California, Berkeley
  • MS, Public Health, University of California, Los Angeles
  • DrPH, Epidemiology, University of California, Los Angeles
  • 数学专业学士，美国加州大学伯克利分校
  • 数学专业硕士，美国加州大学伯克利分校
  • 公共卫生硕士，美国加州大学洛杉矶分校
  • 公共卫生博士(流行病学) ，美国加州大学洛杉矶分校

个人荣誉

桑德 格陵兰博士的工作研究重点包括药物和医疗技术以及传染病学的评估，他最近的工作集中在研究方法和报告的准确性上，这其中包括对2019年新冠病毒疾病的治疗。由于其杰出的领域贡献，多次被评为世界最有影响力研究人员。

2021年11月16日，科睿唯安公司通过收集2010年至2020年12月的引文，列出了世界最有影响力的研究人员，即被引用次数最多的研究人员，这些学者的研究成果在21个科学和社会科学领域的其他科研论文中最常被引用，在他们对应领域排名约前1%，其中包括了来自加州大学洛杉矶分校菲尔丁公共卫生学院的桑德 格陵兰博士[12]。

2021年12月3日，根据另一项新的研究，大约47名现任或前任加州大学洛杉矶分校菲尔丁公共卫生学院的研究人员是1960年至2020年在他们的领域中被引用最多的人之一，其中也包括了桑德格陵兰博士。这项工作由斯坦福大学、爱思唯尔和赛特科技策略的学者共同完成，于2021年第四季度由爱思唯尔出版社出版，至2020年为止一直沿用至今[13]。

学术成果

代表论文:

  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.

书籍：

  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.

参考文献

  1. ↑ 1.0 1.1 Greenland, S. (March 1989). "Modeling and variable selection in epidemiologic analysis". American Journal of Public Health. 79 (3): 340–9. doi:10.2105/AJPH.79.3.340. PMC 1349563. PMID 2916724.
  2. ↑ 2.0 2.1 Greenland, Sander; Pearl, Judea; Robins, James M. (January 1999). "Causal Diagrams for Epidemiologic Research". Epidemiology. 10 (1): 37–48. doi:10.1097/00001648-199901000-00008. ISSN 1044-3983. PMID 9888278.
  3. ↑ 3.0 3.1 Greenland, S. (January 1, 2005). "Multiple-bias modeling for analysis of observational data (with discussion)". Journal of the Royal Statistical Society. Series A (Statistics in Society). 168 (2): 267–308. doi:10.1111/j.1467-985x.2004.00349.x.
  4. ↑ 4.0 4.1 Rothman, K. J.; Greenland, S.; Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN 978-0-7817-5564-1. 
  5. ↑ 5.0 5.1 "ASA Fellows". American Statistical Association. Retrieved 2011-02-15.
  6. ↑ 6.0 6.1 Porta, M., ed. (2014). A Dictionary of Epidemiology (6th ed.). New York: Oxford University Press. ISBN 9780199976737. http://global.oup.com/academic/product/a-dictionary-of-epidemiology-9780199976737?cc=us&lang=en. 
  7. ↑ 7.0 7.1 Amrhein, V.; Greenland, S.; McShane, B. (March 2019). "Scientists rise up against statistical significance". Nature. 567 (7748): 305–307. Bibcode:2019Natur.567..305A. doi:10.1038/d41586-019-00857-9. PMID 30894741. S2CID 84186074.
  8. ↑ 8.0 8.1 Amrhein, V.; Greenland, S. (January 2018). "Remove, rather than redefine, statistical significance". Nature Human Behaviour. 2 (1): 4. doi:10.1038/s41562-017-0224-0. PMID 30980046. S2CID 46814177.
  9. ↑ 9.0 9.1 ""Abandon / Retire Statistical Significance": Your chance to sign a petition!" (in English).
  10. ↑ 10.0 10.1 Rafi, Z; Greenland, S (September 2020). "Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise". BMC Medical Research Methodology. 20 (1): 244. arXiv:1909.08579. doi:10.1186/s12874-020-01105-9. PMC 7528258. PMID 32998683.
  11. ↑ 11.0 11.1 Greenland, S.; Senn, S. J.; Rothman, K. J.; Carlin, J. B.; Poole, C.; Goodman, S. N.; Altman, D. G. (April 2016). "Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations". European Journal of Epidemiology. 31 (4): 337–50. doi:10.1007/s10654-016-0149-3. PMC 4877414. PMID 27209009.
  12. ↑ Five UCLA Fielding School of Public Health Scholars Among Most Highly Cited Researchers for 2021 | Jonathan and Karin Fielding School of Public Health
  13. ↑ Forty-seven Current or Former UCLA Fielding School Scholars Among Most Highly Cited, 1960-2020 | Jonathan and Karin Fielding School of Public Health

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相关路径

Category:American statisticians Category:American epidemiologists Category:1951 births Category:Living people Category:Fellows of the American Statistical Association

类别: 美国统计学家类别: 美国流行病学家类别: 1951年出生类别: 活人类别: 美国统计协会研究员


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