Statistical Mistakes

Misuse of statistical inference in medical research


  1. Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med 2014;33:1242–1258.
  2. Christenfeld NJS, Sloan RP, Carrol D, Greenland S. Risk factors, confounding, and the illusion of statistical control. Psychosom Med 2004;66:868-875.
  3. Cole SR, Platt RW, Schisterman EF, Haitao C, Westreich D, Richardson D, Poole C. Illustrating bias due to conditioning on a collider. Int J Epidemiol 2010;39:417-420.
  4. Glymour MM, Weuve J, Berkman LF, et al. When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol 2005;162:267-78 (see also Cologne B. RE: When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol 2006;164:1138-1140).
  5. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 2003; 14:300–306.
  6. Greenland S, Pearce N. Statistical foundations for model-based adjustments. Annu Rev Public Health 2015;36:89-108.
  7. Hanley JA, Foster BJ. Avoiding blunders involving ‘immortal time’. Int J Epidemiol 2014;43:949-961.
  8. Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. Am J Epidemiol 2002;155:176-184.
  9. Kahan BC. Accounting for centre-effects in multicentre trials with a binary outcome – when, why, and how? BMC Medical Research Methodology 2014,14:20.
  10. Malek MM, Berger DE, Coburn JW. On the inappropriateness of stepwise regression for model building and testing. Eur J Appl Physiol 2007;101:263-264.
  11. Pearl J. Remarks on the method of propensity score. Stat Med 2009;28:1415–1424.
  12. Ranstam J, Cook JA. Causal relationship and confounding in statistical models. Br J Surg. 2016 Oct;103(11):1445-6.
  13. Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiological studies. Epidemiol 2009;20:488-495.
  14. Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol 2008;8:70.
  15. Shmueli G. To Explain or to Predict? Statistical Science 2010;25:289–310.
  16. Signorello LB, McLaughlin JK, Lipworth L, Friis S, Sørensen HT, Blot WJ. Confounding by indication in epidemiologic studies of commonly used analgesics. Am J Ther 2002;9:199-205.
  17. Schuster T, Lowea WK, Platt RW. Propensity score model overfitting led to inflated variance of estimated odds ratios. J Clin Epidemiol 2016 September, Doi:10.1016/j.jclinepi.2016.05.017.
  18. Sjölander A. Propensity scores and M-structures. Stat Med 2009;28:1416–1420.
  19. Sjölander A, Greenland S. Ignoring the matching variables in cohort studies – when is it valid and why? Stat Med 2013;32:4696-4708.
  20. Tu Y-K, Gunnell D, Gilthorpe MS. Simpson’s Paradox, Lord’s Paradox, and Suppression Effects are the same phenomenon – the reversal paradox. Emerging Themes in Epidemiology 2008, 5:2.
  21. Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol 2013;177:292-298.