Clinical Trials: Handling the Data

  • Douglas S. Swords
  • Benjamin S. BrookeEmail author
Part of the Success in Academic Surgery book series (SIAS)


Clinical trials play an important role in establishing the efficacy of different surgical interventions. It is important to understand the methodological considerations that are inherent to the design, analysis, and reporting of surgical trials. This chapter reviews the essentials that surgical investigators need to know in order to handle data from clinical trials.


Clinical trials Hypothesis testing Bias Error Missing data Statistical analysis 


  1. 1.
    Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019;567(7748):305–7.CrossRefGoogle Scholar
  2. 2.
    Ionnidis JPA. Retiring statistical significance would give bias a free pass. Nature. 2019;567(7749):461.CrossRefGoogle Scholar
  3. 3.
    Ioannidis JPA. The proposal to lower P value thresholds to .005. JAMA. 2018;319(14):1429–30.CrossRefGoogle Scholar
  4. 4.
    Chavalarias D, Wallach JD, Li AHT, Ioannidis JPA. Evolution of reporting P values in the biomedical literature, 1990-2015. JAMA. 2016;315(11)Google Scholar
  5. 5.
    Norton EC, Dowd BE, Maciejewski ML. Odds ratios: current best practices and use. JAMA. 2018;320(1):84–5.CrossRefGoogle Scholar
  6. 6.
    Norton EC, Dowd BE. Log odds and the interpretation of logit models. Health Serv Res. 2018;53(2):859–78.CrossRefGoogle Scholar
  7. 7.
    Norton EC, Miller MM, Kleinman LC. Computing adjusted risk ratios and risk differences in Stata. Stata J. 2013;13(3):492–509.CrossRefGoogle Scholar
  8. 8.
    Uno H, Claggett B, Tian L, et al. Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J Clin Oncol. 2014;32(22):2380–5.CrossRefGoogle Scholar
  9. 9.
    Weir IR, Marshall GD, Schneider JI, et al. Interpretation of time-to-event outcomes in randomized trials: an online randomized experiment. Ann Oncol. 2019;30(1):96–102.CrossRefGoogle Scholar
  10. 10.
    Saquib N, Saquib J, Ioannidis JP. Practices and impact of primary outcome adjustment in randomized controlled trials: meta-epidemiologic study. BMJ. 2013;347:f4313.CrossRefGoogle Scholar
  11. 11.
    Kasenda B, Schandelmaier S, Sun X, et al. Subgroup analyses in randomised controlled trials: cohort study on trial protocols and journal publications. BMJ. 2014;g4539:349.Google Scholar
  12. 12.
    Wallach JD, Sullivan PG, Trepanowski JF, Sainani KL, Steyerberg EW, Ioannidis JP. Evaluation of evidence of statistical support and corroboration of subgroup claims in randomized clinical trials. JAMA Intern Med. 2017;177(4):554–60.CrossRefGoogle Scholar
  13. 13.
    Oxman AD, Guyatt GH. A consumer’s guide to subgroup analyses. Ann Intern Med. 1992;116(1):78–884.CrossRefGoogle Scholar
  14. 14.
    Newgard GD, Lewis RJ. Missing data: how to best account for what is not known. JAMA. 2015;314(9):940–1.CrossRefGoogle Scholar
  15. 15.
    Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. Wiley: Princeton, NJ; 2002.CrossRefGoogle Scholar
  16. 16.
    Royston P. Multiple imputation of missing values. Stata J. 2004;4(3):227–41.CrossRefGoogle Scholar
  17. 17.
    Peng L, Stuart EA, Allison DB. Multiple imputation: a flexible tool for handling missing data. JAMA. 2015;314(18):1966–7.CrossRefGoogle Scholar
  18. 18.
    Graham JW, Olchowski AE, Gilreath TD. How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prev Sci. 2007;8:206–13.CrossRefGoogle Scholar
  19. 19.
    Laupacis A, Sackett DL, Roberts RS. An assessment of clinically useful measures of the consequences of treatment. N Engl J Med. 1988;318:1728–33.CrossRefGoogle Scholar
  20. 20.
    McAlister FA. The “number needed to treat” turns 20—and continues to be used and misused. CMAJ. 2008;179(6):549–53.CrossRefGoogle Scholar
  21. 21.
    Tignanelli CJ, Napolitano LM. The fragility index in randomized clinical trials as a means of optimizing patient care. JAMA Surg. 2018.Google Scholar
  22. 22.
    ClinCalc LLC. Fragility index calculator. Published 2018. Accessed 2 Apr.
  23. 23.
    Schulz KF, Altman DG, Moher D, Guyatt GH. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332.CrossRefGoogle Scholar
  24. 24.
    Begg C, Cho M, Eastwood S, et al. Improving the quality of reporting of randomized controlled trials. The CONSORT statement. JAMA. 1996;276(8):637–9.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Utah Interventional Quality and Implementation Research (U-INQUIRE) Group, Department of SurgeryUniversity of Utah School of MedicineSalt Lake CityUSA

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