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Principles of Adult Surgical Critical Care pp 453–463Cite as

Biostatistics for the Intensivist: A Clinically Oriented Guide to Research Analysis and Interpretation

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Abstract

Statistical analysis is an integral and necessary part of being a clinician. Applying the results of statistical analysis can change clinical practices in a meaningful way. Consequently, this biostatistics chapter has been created to provide a basic understanding of statistics as applied to the analysis of research studies. This chapter outlines the basic mechanics of statistics, describes different study types, and explains statistical testing from a practical perspective.

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References

  1. Slee VN. Statistics influence medical practice. Mod Hosp. 1954;83(1):55–8.

    CAS  PubMed  Google Scholar 

  2. Lancaster HO. Statistics and medical practice. Med J Aust. 1982;1(9):366.

    CAS  PubMed  Google Scholar 

  3. Stawicki SP, et al. Fundamentals of patient safety in medicine & surgery. 1st ed. New Delhi: Wolters Kluwer (India) Pvt. Ltd; 2015.

    Google Scholar 

  4. McCluskey A, Lalkhen AG. Statistics IV: interpreting the results of statistical tests. Contin Educ Anaesth Crit Care Pain. 2007;7(6):208–12.

    Google Scholar 

  5. Lieberman MD, Cunningham WA. Type I and Type II error concerns in fMRI research: re-balancing the scale. Soc Cogn Affect Neurosci. 2009;4(4):423–8.

    PubMed  PubMed Central  Google Scholar 

  6. Greenfield ML, Kuhn JE, Wojtys EM. A statistics primer. P values: probability and clinical significance. Am J Sports Med. 1996;24(6):863–5.

    CAS  PubMed  Google Scholar 

  7. Rosen BL, DeMaria AL. Statistical significance vs. practical significance: an exploration through health education. Am J Health Edu. 2012;43(4):235–41.

    Google Scholar 

  8. Case LD, Ambrosius WT. Power and sample size. Methods Mol Biol. 2007;404:377–408.

    PubMed  Google Scholar 

  9. Freedman KB, Back S, Bernstein J. Sample size and statistical power of randomised, controlled trials in orthopaedics. J Bone Joint Surg Br. 2001;83(3):397–402.

    CAS  PubMed  Google Scholar 

  10. Jaykaran PY, Kantharia ND. Reporting of sample size and power in negative clinical trials published in Indian medical journals. J Pharm Negat Results. 2011;2(2):87–90.

    Google Scholar 

  11. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: L. Erlbaum Associates; 1988. p. 567.

    Google Scholar 

  12. Boxer PJ. Notes on Checkland’s soft systems methodology. 1994 [May 3, 2015]; Available from: http://web.archive.org/web/20091229162231/http://www.brl.com/pdfs/checklnd.pdf.

  13. GraphPad. What is the difference between Type I, Type II, and Type III errors? 2015 [May 3, 2015]; Available from: http://graphpad.com/support/faqid/1080/.

  14. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2008. p. 758. x.

    Google Scholar 

  15. Hill HA, Kleinbaum DG. Bias in observational studies. Encyclopedia Biostatistics. 2005. 1.

    Google Scholar 

  16. Sica GT. Bias in research studies. Radiology. 2006;238(3):780–9.

    PubMed  Google Scholar 

  17. Pannucci CJ, Wilkins EG. Identifying and avoiding bias in research. Plast Reconstr Surg. 2010;126(2):619–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Duffy SW, et al. Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. Am J Epidemiol. 2008;168(1):98–104.

    PubMed  Google Scholar 

  19. Hennessy S, et al. Factors influencing the optimal control-to-case ratio in matched case-control studies. Am J Epidemiol. 1999;149(2):195–7.

    CAS  PubMed  Google Scholar 

  20. ResearchGate. What is the rationale for 1:2 ratio in case-control studies? 2015 [May 3, 2015]; Available from: https://www.researchgate.net/post/What_is_the_Rationale_for_12_ratio_in_Case_Control_studies.

  21. Grimes DA, Schulz KF. Descriptive studies: what they can and cannot do. Lancet. 2002;359(9301):145–9.

    PubMed  Google Scholar 

  22. Zollinger RM, Ellison EH. Primary peptic ulcerations of the jejunum associated with islet cell tumors of the pancreas. Ann Surg. 1955;142(4):709–23; discussion, 724–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Grimes DA, Schulz KF. An overview of clinical research: the lay of the land. Lancet. 2002;359(9300):57–61.

    PubMed  Google Scholar 

  24. Grimes DA, Schulz KF. Cohort studies: marching towards outcomes. Lancet. 2002;359(9303):341–5.

    PubMed  Google Scholar 

  25. Barton S. Which clinical studies provide the best evidence? The best RCT still trumps the best observational study. BMJ. 2000;321(7256):255–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Ioannidis JP. Interpretation of tests of heterogeneity and bias in meta-analysis. J Eval Clin Pract. 2008;14(5):951–7.

    PubMed  Google Scholar 

  27. McNutt LA, et al. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003;157(10):940–3.

    PubMed  Google Scholar 

  28. Wilber ST, Fu R. Risk ratios and odds ratios for common events in cross-sectional and cohort studies. Acad Emerg Med. 2010;17(6):649–51.

    PubMed  Google Scholar 

  29. Stawicki SP. Guide to free statistical software: general applications. OPUS 12 Sci. 2010;4(1):12–3.

    Google Scholar 

  30. Stawicki SP. Guide to statistical software: meta-analysis applications. OPUS 12 Sci. 2011;5(1):4–5.

    Google Scholar 

  31. Larson MG. Descriptive statistics and graphical displays. Circulation. 2006;114(1):76–81.

    PubMed  Google Scholar 

  32. Onchiri S. Conceptual model on application of chi-square test in education and social sciences. Educ Res Rev. 2013;8(15):1231–41.

    Google Scholar 

  33. Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet. 2006;7(10):781–91.

    CAS  PubMed  Google Scholar 

  34. Hosmer DW, Lemeshow S. Applied logistic regression, Wiley series in probability and statistics texts and references section. 2nd ed. New York: Wiley; 2000. p. 373. xii.

    Google Scholar 

  35. Stratford PW. The added value of confidence intervals. Phys Ther. 2010;90(3):333–5.

    PubMed  Google Scholar 

  36. McCluskey A, Lalkhen AG. Statistics II: central tendency and spread of data. Contin Educ Anaesth Crit Care Pain. 2007;7(4):127–30.

    Google Scholar 

  37. Salmond S. Taking the mystery out of research: box and whisker plots: displaying mean, interquartile range, and range. Orthop Nurs. 2007;26(1):33.

    PubMed  Google Scholar 

  38. Wan X, et al. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14(1):135.

    PubMed  PubMed Central  Google Scholar 

  39. Lyon A. Why are normal distributions normal? Br J Philos Sci. 2014;65(3):621–49.

    Google Scholar 

  40. Barde MP, Barde PJ. What to use to express the variability of data: standard deviation or standard error of mean? Perspect Clin Res. 2012;3(3):113–6.

    PubMed  PubMed Central  Google Scholar 

  41. Curran-Everett D. Explorations in statistics: standard deviations and standard errors. Adv Physiol Educ. 2008;32(3):203–8.

    PubMed  Google Scholar 

  42. Sculco AD. Spine stats. The Kruskal-Wallis and Wilcoxon-Mann Whitney tests. Spine J. 2001;1(2):153.

    CAS  PubMed  Google Scholar 

  43. Hollander M, Wolfe DA. Nonparametric statistical methods, Wiley series in probability and statistics texts and references section. 2nd ed. New York: Wiley; 1999. p. 787. xiv.

    Google Scholar 

  44. Lazic SE. Why we should use simpler models if the data allow this: relevance for ANOVA designs in experimental biology. BMC Physiol. 2008;8(1):16.

    PubMed  PubMed Central  Google Scholar 

  45. Tabachnick BG, Fidell LS. Experimental designs using ANOVA. Belmont: Thomson/Brooks/Cole; 2007. p. 724. xxi.

    Google Scholar 

  46. Bewick V, Cheek L, Ball J. Statistics review 7: correlation and regression. Crit Care. 2003;7(6):451–9.

    PubMed  PubMed Central  Google Scholar 

  47. Curran-Everett D. Explorations in statistics: regression. Adv Physiol Educ. 2011;35(4):347–52.

    PubMed  Google Scholar 

  48. Tabachnick BG, Fidell LS. Using multivariate statistics. 5th ed. Boston: Pearson/Allyn & Bacon; 2007. p. 980. xxviii.

    Google Scholar 

  49. Curran-Everett D. Explorations in statistics: confidence intervals. Adv Physiol Educ. 2009;33(2):87–90.

    PubMed  Google Scholar 

  50. Wikipedia. 68–95–99.7 rule. 2015 [May 3, 2015]; Available from: http://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule.

  51. Parikh R, et al. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol. 2008;56(1):45–50.

    PubMed  PubMed Central  Google Scholar 

  52. Bruno P. The importance of diagnostic test parameters in the interpretation of clinical test findings: the prone hip extension test as an example. J Can Chiropr Assoc. 2011;55(2):69–75.

    PubMed  PubMed Central  Google Scholar 

  53. Fawcett T. An introduction to ROC analysis. Pattern Recog Lett. 2006;27(8):861–74.

    Google Scholar 

  54. Pett MA. Nonparametric statistics for health care research: statistics for small samples and unusual distributions. Thousand Oaks: Sage Publications; 1997. p. 307.

    Google Scholar 

  55. Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med. 2005;37(5):360–3.

    PubMed  Google Scholar 

  56. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–74.

    CAS  PubMed  Google Scholar 

  57. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86(2):420–8.

    CAS  PubMed  Google Scholar 

  58. Bewick V, Cheek L, Ball J. Statistics review 12: survival analysis. Crit Care. 2004;8(5):389–94.

    PubMed  PubMed Central  Google Scholar 

  59. UCLA. Supplemental notes to applied survival analysis: applied survival analysis. 2015 [May 3, 2015]; Available from: http://www.ats.ucla.edu/stat/examples/asa/test_proportionality.htm.

  60. Lee ET. Statistical methods for survival data analysis, Wiley series in probability and mathematical statistics applied probability and statistics. 2nd ed. New York: Wiley; 1992. p. 482. xii.

    Google Scholar 

  61. Conley EC, et al. Simultaneous trend analysis for evaluating outcomes in patient-centred health monitoring services. Health Care Manag Sci. 2008;11(2):152–66.

    PubMed  Google Scholar 

  62. Stawicki SP. Financial analysis techniques in clinical practice: from ‘micro’ to ‘macro’. OPUS 12 Sci. 2008;2(3):3–9.

    Google Scholar 

  63. Stawicki SP, et al. The glucogram: a new quantitative tool for glycemic analysis in the surgical intensive care unit. Int J Crit Illn Inj Sci. 2011;1(1):5–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Stawicki SP. Application of financial analysis techniques to clinical laboratory data: a novel method of trend interpretation in the intensive care unit. OPUS 12 Sci. 2007;1(2):1–4.

    Google Scholar 

  65. Stawicki SP. Application of financial analysis techniques to vital sign data: a novel method of trend interpretation in the intensive care unit. OPUS 12 Sci. 2007;1(1):14–6.

    Google Scholar 

  66. Pappada SM, et al. Evaluation of a model for glycemic prediction in critically ill surgical patients. PLoS One. 2013;8(7):e69475.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Rosenberg, D. U. S. Department of health & human services, division of science, education and analysis: trend analysis and interpretation. 1997; Available from: http://mchb.hrsa.gov/publications/pdfs/trendanaylsis.pdf.

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Authors and Affiliations

  1. Department of Surgery, St. Luke’s, University Hospital, Bethlehem, PA, 18015, USA

    Heidi H. Hon MD

  2. Temple University School of Medicine, The Research Institute, St. Luke’s University Health Network, Bethlehem, PA, 18015, USA

    Jill C. Stoltzfus PhD

  3. Department of Research and Innovation, St. Luke’s University Health Network, Bethlehem, PA, 18015, USA

    Stanislaw P. Stawicki MD, MBA

Authors
  1. Heidi H. Hon MD
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  2. Jill C. Stoltzfus PhD
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  3. Stanislaw P. Stawicki MD, MBA
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Corresponding author

Correspondence to Heidi H. Hon MD .

Editor information

Editors and Affiliations

  1. Trauma, Surgical Critical Care, Emergency Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Niels D. Martin

  2. Trauma, Surgical Critical Care, Emergency Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Lewis J. Kaplan

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Hon, H.H., Stoltzfus, J.C., Stawicki, S.P. (2016). Biostatistics for the Intensivist: A Clinically Oriented Guide to Research Analysis and Interpretation. In: Martin, N.D., Kaplan, L.J. (eds) Principles of Adult Surgical Critical Care. Springer, Cham. https://doi.org/10.1007/978-3-319-33341-0_39

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  • DOI: https://doi.org/10.1007/978-3-319-33341-0_39

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  • Online ISBN: 978-3-319-33341-0

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