This is just a collection of resources that I quote frequently and/or use for teaching. It should give you a rough idea of my work and interests as applied statistician. See also the rassegna section.

10 commandments

The 10 commandments of the statistician [html]


  • Bland JM & Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307 [paper]
  • Bland JM & Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 1995;346:1085 [paper]
  • Bland JM & Altman DG. Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol 2003;22:85 [paper]
  • Hopkins WG. Bias in Bland-Altman but not Regression Validity Analyses. Sportscience 2004;8:42 [paper] [comment]
  • Carstensen B. Comparing clinical measurement methods. Wiley: 2011 [html]
  • Uebersax J. Statistical methods for rater agreement [html]

Classics (by year)

  • Cornfield J et al. Smoking and lung cancer: recent evidence and a discussion of some questions. 1959. Int J Epidemiol. 2009;38:1175 [paper].
  • Tukey J. The future of data analysis. Ann Math Statist. 1962;33:1 [paper].
  • Diaconis &, Mosteller F. Methods for studying coincidences. JASA 1989;84:108 [paper].
  • Velleman PF, Wilkinson L. Nominal, ordinal, interval, and ratio typologies are misleading. Am Stat. 1993;47:65 [paper].
  • Cornfield J. Principles of research. 1959. Statist. Med. 2012;31:2760 [abstract].
  • Van Belle G. Some extremes in client types, their characteristics and the expected roles of the statistical consultant [paper].

Diagnostic tests

  • Pepe MS. The diagnostic and biomarkers statistical (DABS) center [html]

Exact methods

  • Mehta CR, Patel NR. Exact logistic regression: theory and examples. Stat Med. 1995;14:2143 [paper]
  • Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21:2409 [paper]
  • Heinze G. A comparative investigation of methods for logistic regression with separated or nearly separated data. Stat Med. 2006;25:4216 [abstract]

Longitudinal data analysis

  • Laird NM & Ware JH. Random-effects models for longitudinal data. Biometrics 1982;38:963 [paper]
  • Liang KY & Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986;73:1513 [paper]
  • Rabe-Hesketh S. Generalised Linear Latent And Mixed Models (GLLAMM) [html]
  • Gardiner JC, Luo Z, Roman LA. Fixed effects, random effects and GEE: what are the differences? Stat Med. 2009;28:221 [paper]

Multivariable modeling

  • Harrell F. Problems Caused by Categorizing Continuous Variables [html]
  • Hoaglin D. Making sense of coefficients in mulitple regression [html] [stata journal]
  • Royston P, Sauberbrei W. Multivariable model building [html]

Ordinal data analysis

  • Ananth CV & Kleinbaum DG. Regression models for ordinal responses: a review of methods and applications. Int J Epidemiol 1997;26:1323 [paper]

Philosophy of statistics

  • Gelman A, and Shalizi CR. Philosophy and the practice of Bayesian statistics. Br J Math Stat Psychol 2013;66:8-38 [paper].
  • Gelman A, and Robert CP. “Not Only Defended But Also Applied”: The Perceived Absurdity of Bayesian Inference. The American Statistician 2013;67:1-5 [paper].
  • Gigerenzer G, Marewski JN. Surrogate Science: the idol of a universal method for scientific inference. Journal of Management 2015;41:421–440 [paper].
  • Senn S. You may believe you are a Bayesian but you are probably wrong. Rationality, Markets and Morals 2011;2(42) [paper].
  • Mayo DG. The error-statistical philosophy and the practice of Bayesian statistics: comments on Gelman and Shalizi: 'Philosophy and the practice of Bayesian statistics'. Br J Math Stat Psychol 2013;66:57-64 [paper].
  • Mayo DG. Discussion: Bayesian Methods: Applied? Yes. Philosophical Defense? In Flux. The American Statistician. 2013;67:11-15 [html].
  • Mayo DG, and Spano A. Error Statistics. In: Bandyopadhyay PS, and Forster MR., eds. Handbook of Philosophy of Science. Volume 7. Philosophy of Statistics. Elsevier; 2011. p. 1-46 [paper].

Relative risk regression

  • Barros AJ & Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol 2003;3:21 [paper]
  • Lumley T et al. Relative risk regression in medical research: models, contrasts, estimators and algorithms. UW Biostatistics working paper series. 2006;203 [paper]

Sample size

  • Lenth RV. Some practical guidelines for effective sample size determination. Am Stat. 2001;55:187-193 [paper].
  • Lenth RV. Post hoc power: tables and commentary. 2007 [paper].
  • Gelman A & Loken E. The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time [paper].
  • Gelman A & Carlin J. Beyond power calculations: Assessing Type S (sign) and Type M (magnitude) errors. Perspectives on Psychological Science. 2014;1:11 [paper].
  • Senn, S Delta Force: To what extent is clinical relevance relevant [html]


  • Iacus SM. Multivariate Matching Methods That Are Monotonic Imbalance Bounding. J Am Stat Assoc. 2011;106:345-361 [paper]
  • Iacus M, King G, Porro G. Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis 2012;20:1-24 [paper]

Missing data

  • Carpenter J, Bartlett J, Kenward M. Missing data [web page]