Advertisement

Adaptive Three-Stage Clinical Trial Design for a Binary Endpoint in the Rare Disease Setting

  • Lingrui Gan
  • Zhaowei HuaEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 218)

Abstract

A fundamental challenge in developing therapeutic agents for rare diseases is the limited number of eligible patients. A conventional randomized clinical trial may not be adequately powered if the sample size is small and asymptotic assumptions needed to apply common test statistics are violated. This paper proposes an adaptive three-stage clinical trial design for a binary endpoint in the rare disease setting. It presents an exact unconditional test statistic to generally control Type I error when sample size is small while not sacrificing power. Adaptive randomization has the potential to increase power by allocating greater numbers of patients to a more effective treatment. Performance of the method is illustrated using simulation studies.

Keywords

Rare disease Small clinical trial Z-pooled unconditional Test Type I error Combination method 

References

  1. 1.
    European Commission: Useful information on rare diseases from an EU perspective. Accessed 19 May 2009Google Scholar
  2. 2.
    Piantadosi, S.: Cross-over designs. In: Clinical Trials, A Methodologic Perspective. Wiley, Toronto (1997)Google Scholar
  3. 3.
    Guyatt, G.H., Heyting, A., Jaeschke, R., Keller, J., Adachi, J.D., Roberts, R.S.: N of 1 randomized trials for investigating new drugs. Control. Clin. Trials 11(2), 88–100 (1990)CrossRefGoogle Scholar
  4. 4.
    Temple, R.J.: Special study designs: early escape, enrichment, studies in non-responders. Commun. Stat. Theory Methods 23(2), 499–530 (1994)CrossRefGoogle Scholar
  5. 5.
    Temple, R.J.: Problems in interpreting active control equivalence trials. Account. Res. 4(3–4), 267–275 (1996)CrossRefGoogle Scholar
  6. 6.
    Rosenberger, W.: Randomized play-the-winner clinical trials: review and recommendations. Control. Clin. Trials 20, 328–342 (1999)CrossRefGoogle Scholar
  7. 7.
    Cook, J.D.: Error in the normal approximation to the t distribution. https://www.johndcook.com/blog/normal_approx_to_t/. Accessed 18 Jan 2017
  8. 8.
    Rao, C.R.: Linear Statistical Inference and its Applications. Wiley, New York (1965)zbMATHGoogle Scholar
  9. 9.
    Honkanen, V.E., Siegel, A.F., Szalai, J.P., Berger, V., Feldman, B.M., Siegel, J.N.: A three-stage clinical trial design for rare disorders. Stat. Med. 20, 3009–3021 (2001)CrossRefGoogle Scholar
  10. 10.
    Cook, J.D., Nadarajah, S.: Stochastic inequality probabilities for adaptively randomized clinical trials. Biom. J. 48, 356–365 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wathen, J.K., Cook, J.D.: Power and bias in adaptively randomized clinical trials. Technical Report UTMDABTR-002-06. Accessed 7 Mar 2006Google Scholar
  12. 12.
    Lydersen, S., Fagerland, M.W., Laake, P.: Recommended tests for association in \(2 \times 2\) tables. Stat. Med. 28, 1159–1175 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Berger, R.L., Boos, D.D.: P values maximized over a confidence set for the nuisance parameter. J. Am. Stat. Assoc. 89, 1012–1016 (1994)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Mehrotra, D.V., Chan, I.S., Berger, R.L.: A cautionary note on exact unconditional inference for a difference between two independent binomial proportions. Biometrics 59, 441–450 (2003)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Bauer, P., Kohne, K.: Evaluation of experiments with adaptive interim analyses. Biometrics 50, 1029–1041 (1994)CrossRefGoogle Scholar
  16. 16.
    Stouffer, S.A., Lumsdaine, A.A., Lumsdaine, M.H., Williams, R.M., Smith, M.B., Janis, I.L., Star, S.A., Cottrell, L.S.: The American soldier: combat and its aftermath. Studies in Social Psychology in World War II., vol. 2. Princeton University Press, Princeton (1949)Google Scholar
  17. 17.
    Abelson, R.P.: Statistics as Principled Argument. Psychology Press, New York (1995)Google Scholar
  18. 18.
    Berger, R.L., Boos, D.D.: P values maximized over a confidence set for the nuisance parameter. J. Am. Stat. Assoc. 89(427), 1012–1016 (1994)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Jeffreys, H.: An invariant form for the prior probability in estimation problems. In: Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences, pp. 453–461 (1946)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of Illinois at Urbana-ChampaignChampaignUSA
  2. 2.Alnylam Pharmaceuticals, Inc.CambridgeUSA

Personalised recommendations