Advertisement

Sample Size Calculation in Oncology Studies

  • Rachel P. Riechelmann
  • Raphael L. C. Araújo
  • Benjamin Haaland
Chapter

Abstract

Sample size calculation is at the core of study design. It is defined as the calculation of the minimum number of subjects to be included in a study in order to detect a true effect or value and must always to be performed a priori. Several aspects have to be considered when computing a sample size, including assumptions of expected outcomes in the control and experimental groups, type I and II error rates, power, and the dropout rate. Without proper sample size calculation, the results of a clinical study can be misleading, not generalizable to other settings, more likely to be false negative or false positive, and might even be associated with ethical implications. Additionally, careful planning and accurate reporting of this calculation ensures transparency and reliability and allows the reproducibility of results.

Keywords

Clinical trials Randomized clinical trials Oncology Cancer Sample size 

References

  1. 1.
    Clark TG, Bradburn MJ, Love SB, et al. Survival analysis part I: basic concepts and first analyses. Br J Cancer. 2003;89:232–8.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Guller U, Oertli D. Sample size matters: a guide for surgeons. World J Surg. 2005;29(5):601.CrossRefPubMedGoogle Scholar
  3. 3.
    Dubey SD. Some thoughts on the one-sided and two-sided tests. J Biopharm Stat. 1991;1:139–50.CrossRefPubMedGoogle Scholar
  4. 4.
    Bariani GM, de Celis Ferrari AC, Precivale M, et al. Sample size calculation in oncology trials: quality of reporting and implications for clinical cancer research. Am J Clin Oncol. 2015;38:570–4.CrossRefPubMedGoogle Scholar
  5. 5.
    Fleming TR. One-sample multiple testing procedure for phase II clinical trials. Biometrics. 1982;38:143–51.CrossRefPubMedGoogle Scholar
  6. 6.
    Gehan EA. The determination of the number of patients required in a preliminary and a follow-up trial of a new chemotherapeutic agent. J Chronic Dis. 1961;13:346–53.CrossRefPubMedGoogle Scholar
  7. 7.
    Simon R. Optimal two-stage designs for phase II clinical trials. Control Clin Trials. 1989;10:1–10.CrossRefPubMedGoogle Scholar
  8. 8.
    Saad ED, Sasse EC, Borghesi G, et al. Formal statistical testing and inference in randomized phase II trials in medical oncology. Am J Clin Oncol. 2013;36:143–5.CrossRefPubMedGoogle Scholar
  9. 9.
    Simon R, Wittes RE, Ellenberg SS. Randomized phase II clinical trials. Cancer Treat Rep. 1985;69:1375–81.PubMedGoogle Scholar
  10. 10.
    Waddell T, Chau I, Cunningham D, et al. Epirubicin, oxaliplatin, and capecitabine with or without panitumumab for patients with previously untreated advanced oesophagogastric cancer (REAL3): a randomised, open-label phase 3 trial. Lancet Oncol. 2013;14:481–9.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Riechelmann RP, Alex A, Cruz L, et al. Non-inferiority cancer clinical trials: scope and purposes underlying their design. Ann Oncol. 2013;24(7):1942.CrossRefPubMedGoogle Scholar
  12. 12.
    Walker E, Nowacki AS. Understanding equivalence and noninferiority testing. J Gen Intern Med. 2011;26:192–6.CrossRefPubMedGoogle Scholar
  13. 13.
    Montgomery AA, Peters TJ, Little P. Design, analysis and presentation of factorial randomised controlled trials. BMC Med Res Methodol. 2003;3:26.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Green S, Liu PY, O’Sullivan J. Factorial design considerations. J Clin Oncol. 2002;20:3424–30.CrossRefPubMedGoogle Scholar
  15. 15.
    Bosset JF, Collette L, Calais G, et al. Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med. 2006;355:1114–23.CrossRefPubMedGoogle Scholar
  16. 16.
    Cunningham D, Starling N, Rao S, et al. Capecitabine and oxaliplatin for advanced esophagogastric cancer. N Engl J Med. 2008;358:36–46.CrossRefPubMedGoogle Scholar
  17. 17.
    Kernan WN, Viscoli CM, Makuch RW, et al. Stratified randomization for clinical trials. J Clin Epidemiol. 1999;52:19–26.CrossRefPubMedGoogle Scholar
  18. 18.
    Mayer RJ, Van Cutsem E, Falcone A, et al. Randomized trial of TAS-102 for refractory metastatic colorectal cancer. N Engl J Med. 2015;372:1909–19.CrossRefPubMedGoogle Scholar
  19. 19.
    Green SJ, Fleming TR, O’Fallon JR. Policies for study monitoring and interim reporting of results. J Clin Oncol. 1987;5:1477–84.CrossRefPubMedGoogle Scholar
  20. 20.
    Posch M, Bauer P, Brannath W. Issues in designing flexible trials. Stat Med. 2003;22:953–69.CrossRefPubMedGoogle Scholar
  21. 21.
    Arya R, Antonisamy B, Kumar S. Sample size estimation in prevalence studies. Indian J Pediatr. 2012;79:1482–8.CrossRefPubMedGoogle Scholar
  22. 22.
    Halpern SD, Karlawish JH, Berlin JA. The continuing unethical conduct of underpowered clinical trials. JAMA. 2002;288:358–62.CrossRefPubMedGoogle Scholar
  23. 23.
    Altman DG. Statistics and ethics in medical research: III How large a sample? Br Med J. 1980;281:1336–8.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Schulz KF, Altman DG, Moher D, et al. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomized trials. Open Med. 2010;4:e60–8.PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rachel P. Riechelmann
    • 1
  • Raphael L. C. Araújo
    • 2
  • Benjamin Haaland
    • 3
  1. 1.Department of Clinical Oncology, AC Camargo Cancer CenterSão PauloBrazil
  2. 2.Department of Upper Gastrointestinal and Hepato-Pancreato-Biliary SurgeryBarretos Cancer HospitalBarretosBrazil
  3. 3.University of UtahSalt Lake CityUSA

Personalised recommendations