Avoiding biostatistical pitfalls in the design and analysis of head and neck cancer clinical trials

  • Robert Makuch
  • Mary Johnson
Part of the Cancer Treatment and Research book series (CTAR, volume 32)


The ultimate objective of any clinical trial is to obtain the correct answer to an important medical question. The science of biostatistics plays an important role in helping to meet this fundamental objective. By properly applying sound statistical principles to clinical research in oncology, one can insure that results of completed studies are valid and convincing to others in the scientific community. To this end, the biomedical literature contains several excellent and thorough discussions regarding the design, execution, and analysis of cancer clinical trials and methods of data acquisition [1–3]. These references draw attention to certain basic components of a successful clinical trial, including: (1) a clear, unambiguous protocol which addresses a significant medical question, (2) well-defined conditions for entry of patients on-study, (3) sample sizes sufficiently large and duration of follow-up adequate to detect treatment effects if they are present, (4) a clear description of treatment regimens and experimental design, (5) explicit definition of endpoints used for efficacy and safety evaluation, (6) patient record forms and data management procedures which enhance data quality, (7) appropriate methodology for data monitoring and statistical analysis to account for incomplete data, and (8) appropriate consideration of ethical issues. While there may be a variety of equally plausible ways to satisfy each of these criteria, their precise specification will depend on the goals of the particular trial, its administrative structure, and the nature of the treatment and disease under study.


Neck Cancer Induction Chemotherapy Treatment Difference Treatment Assignment Cancer Clinical Trial 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Buyse, M.E., Staquet, M.J., Sylvester, R.J. 1984. Cancer Clinical Trials: Methods and Practice. Oxford University Press, Oxford.Google Scholar
  2. 2.
    Mike, V., Stanley, K.E. 1982. Statistics in Medical Research: Methods and Issues, with Applications in Cancer Research. John Wiley and Sons, Inc., New York.Google Scholar
  3. 3.
    Symposium on Methodology and Quality Assurance in Cancer Clinical Trials. 1985. Cancer Treat. Rep. 69: 1039–1233.Google Scholar
  4. 4.
    Gehan, E.A., Freireich, E.J. 1974. Non-randomized controls in cancer clinical trials. N. Engl. J. Med. 290: 198–203.PubMedCrossRefGoogle Scholar
  5. 5.
    Byar, D.P., Simon, R.M., Friedewald, W.T. et al. 1976. Randomized clinical trials. N. Engl. J. Med. 295: 74–80.PubMedCrossRefGoogle Scholar
  6. 6.
    Farewell, V.T., D’Angio, G.J. 1981. A simulated study of historical controls using real data. Biometrics 37: 169–176.PubMedCrossRefGoogle Scholar
  7. 7.
    Pocock, S.J. 1977. Randomized clinical trials (letter). Br. Med. J. 1 (6077): 1661.PubMedCrossRefGoogle Scholar
  8. 8.
    Byar, D.P. 1979. Necessity and justification of randomized clinical studies. In: Controversies in Cancer Treatment (H.J. Tagnon, M.J. Staquet, eds.). Mason Publishing, New York, pp. 75–82.Google Scholar
  9. 9.
    Zelen, M. 1979. A new design for randomized clinical trials. N. Engl. J. Med. 300: 1273–1275. 10.Google Scholar
  10. 10.
    Schoenfeld, D., Gelber, R. 1979. Designing and analyzing clinical trials which allow institutions to randomize patients to a subset of the treatments under study. Biometrics 35: 825–829.PubMedCrossRefGoogle Scholar
  11. 11.
    Makuch, R.W., Simon, R.M. 1978. A note on the design of multi-institution three-treatment studies. Cancer Clin. Trials 1: 301–303.Google Scholar
  12. 12.
    Peto, R. 1978. Clinical trial methodology. Biomedicine 28: 24–36.PubMedGoogle Scholar
  13. 13.
    Byar, D.P., Piantadosi, S. 1985. Factorial designs for randomized clinical trials. Cancer Treat. Rep. 69: 1055–1062.Google Scholar
  14. 14.
    Freiman, J.A., Chalmers, T.C., Smith, H., Jr., et al 1978. The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial: Survey of 71 ’negative’ trials. N. Engl. J. Med. 299: 690–694.PubMedCrossRefGoogle Scholar
  15. 15.
    Lachin, J.M. 1981. Introduction to sample size determination and power analysis for clinical trials. Controlled Clin. Trials 2: 93–113.Google Scholar
  16. 16.
    Casagrande, J.T., Pike, M.C., Smith, P.G. 1978. An improved formula for calculating sample size for comparing two binomial distributions. Biometrics 34: 483–486.PubMedCrossRefGoogle Scholar
  17. 17.
    Makuch, R.W., Simon, R.M. 1978. Sample size requirements for evaluating a conservative therapy. Cancer Treat. Rep. 62: 1037–1040.Google Scholar
  18. 18.
    Detsky, A.S., Sackett, D.L. 1985. When was a ’negative’ clinical trial big enough? How many patients you needed depends on what you found. Arch. Intern. Med. 145: 709–712.Google Scholar
  19. 19.
    Makuch, R.W., Johnson, M.F. 1986. Some issues in the design and interpretation of ’negative’ clinical studies. Arch. Intern. Med. 146: 986–989.Google Scholar
  20. 20.
    George, S.L., Desu, M.M. 1974. Planning the size and duration of a clinical trial studying the time to some critical event. J. Chronic. Dis. 27: 15–24.PubMedCrossRefGoogle Scholar
  21. 21.
    Makuch, R.W., Simon, R.M. 1982. Sample size requirements for comparing time-to-failure among k treatment groups. J. Chron. Dis. 35: 861–867.PubMedCrossRefGoogle Scholar
  22. 22.
    Schoenfeld, D. 1981. The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika 68: 316–319.CrossRefGoogle Scholar
  23. 23.
    Rubinstein, L.V., Gail, M.H., Santner, T.J. 1981. Planning the duration of a comparative clinical trial with loss to follow-up and a period of continued observation. J. Chronic. Dis. 34: 469–479.PubMedCrossRefGoogle Scholar
  24. 24.
    Palta, M., McHugh, R. 1980. Planning the size of a cohort study in the presence of both losses to follow-up and non-compliance. J. Chron. Dis. 33: 501–512.PubMedCrossRefGoogle Scholar
  25. 25.
    Bernstein, D., Lagakos, S.W. 1978. Sample size and power determination for stratified clinical trials. J. Stat. Comput. Simu. 8: 65–73.CrossRefGoogle Scholar
  26. 26.
    Peto, R., Pike, M.C., Armitage, P. et al 1976. Design and analysis of randomized clinical trials, requiring prolonged observation of each treatment. I. Introduction and design. B. J. Cancer 34: 585–612.Google Scholar
  27. 27.
    Grizzle, J.E. 1982. A note on stratifying versus complete random assignment in clinical trials. Controlled Clin. Trials 3: 365–368.Google Scholar
  28. 28.
    Brown, B.W., Jr. 1980. Statistical controversies in the design of clinical trials - some personal views. Controlled Clin. Trials 1: 13–27.Google Scholar
  29. 29.
    Pocock, S.J., Simon, R. 1975. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 31: 103–115.PubMedCrossRefGoogle Scholar
  30. 30.
    Wei, L.J. 1978. An application of an urn model to the design of sequential controlled clinical trials. J. Am. Stat. Assn. 73: 559–563.CrossRefGoogle Scholar
  31. 31.
    Kaplan, E.L., Meier, P. 1958. Nonparametric estimation from incomplete observations. J. Am. Stat. Assn. 53: 458–481.Google Scholar
  32. 32.
    Glatstein, E., Makuch, R.W. 1984. Illusion and reality: Practical pitfalls in interpreting clinical trials. J. Clin. Oncol. 5: 488–497.Google Scholar
  33. 33.
    Brookmeyer, R., Crowley, J. 1982. A confidence interval for the median survival time. Biometrics 38: 29–41.CrossRefGoogle Scholar
  34. 34.
    Gehan, E.A. 1965. A generalized Wilcoxon test for comparing arbitrarily singly censored samples. Biometrika 52: 203–224.PubMedGoogle Scholar
  35. 35.
    Mantel, N. 1966. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother. Rep. 50: 163–170.Google Scholar
  36. 36.
    Schwartz, D., Lellouch, J. 1967. Explanatory and pragmatic attitudes in therapeutic trials. J. Chronic. Dis. 20: 637–648.PubMedCrossRefGoogle Scholar
  37. 37.
    Peto, R. 1982. Statistical aspects of cancer trials. In: Treatments of Cancer (K.E. Hainan, ed.). Chapman and Hall, London, pp. 867–871.Google Scholar
  38. 38.
    Tukey, J.W. 1977. Some thoughts on clinical trials, especially problems of multiplicity. Science 198: 679–684.PubMedCrossRefGoogle Scholar
  39. 39.
    Gail, M., Simon, R. 1985. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics 41: 361–371.PubMedCrossRefGoogle Scholar
  40. 40.
    Shuster, J., van Eys, J. 1983. Interaction between prognostic factors and treatment. Controlled Clin. Trials 4: 209–214.Google Scholar
  41. 41.
    Byar, D.P. 1985. Assessing apparent treatment-covariate interactions in randomized clinical trials. Stat. Med. 4: 255–263.Google Scholar
  42. 42.
    Ingelfinger, J.A., Mosteller, F., Thibodeau, L.A., Ware, J.H. 1983. Biostatistics in Clinical Medicine. Maillan, New York, pp. 253–260.Google Scholar

Copyright information

© Martinus Nijhoff Publishers, Boston 1987

Authors and Affiliations

  • Robert Makuch
  • Mary Johnson

There are no affiliations available

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