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Statistical Methods in Recent HIV Noninferiority Trials: Reanalysis of 11 Trials

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  • Philippe Flandre

Abstract

Background: In recent years the “noninferiority” trial has emerged as the new standard design for HIV drug development among antiretroviral patients often with a primary endpoint based on the difference in success rates between the two treatment groups. Different statistical methods have been introduced to provide confidence intervals for that difference. The main objective is to investigate whether the choice of the statistical method changes the conclusion of the trials. Methods: We presented 11 trials published in 2010 using a difference in proportions as the primary endpoint. In these trials, 5 different statistical methods have been used to estimate such confidence intervals. The five methods are described and applied to data from the 11 trials. The noninferiority of the new treatment is not demonstrated if the prespecified noninferiority margin it includes in the confidence interval of the treatment difference. Results: Results indicated that confidence intervals can be quite different according to the method used. In many situations, however, conclusions of the trials are not altered because point estimates of the treatment difference were too far from the prespecified noninferiority margins. Nevertheless, in few trials the use of different statistical methods led to different conclusions. In particular the use of “exact” methods can be very confusing. Conclusion: Statistical methods used to estimate confidence intervals in noninferiority trials have a strong impact on the conclusion of such trials.

Suggested Citation

  • Philippe Flandre, 2011. "Statistical Methods in Recent HIV Noninferiority Trials: Reanalysis of 11 Trials," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.
  • Handle: RePEc:plo:pone00:0022871
    DOI: 10.1371/journal.pone.0022871
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    1. Barker L. & Rolka H. & Rolka D. & Brown C., 2001. "Equivalence Testing for Binomial Random Variables: Which Test to Use?," The American Statistician, American Statistical Association, vol. 55, pages 279-287, November.
    2. Alan Agresti & Yongyi Min, 2001. "On Small-Sample Confidence Intervals for Parameters in Discrete Distributions," Biometrics, The International Biometric Society, vol. 57(3), pages 963-971, September.
    3. Ivan S. F. Chan & Zhongxin Zhang, 1999. "Test-Based Exact Confidence Intervals for the Difference of Two Binomial Proportions," Biometrics, The International Biometric Society, vol. 55(4), pages 1202-1209, December.
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