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Confidence intervals for overall response rate difference in the sequential parallel comparison design

Author

Listed:
  • Guogen Shan

    (University of Florida)

  • Xinlin Lu

    (University of Florida)

  • Yahui Zhang

    (University of Florida)

  • Samuel S. Wu

    (University of Florida)

Abstract

High placebo responses could significantly reduce the treatment effect in a parallel randomized trial. To combat that challenge, several approaches were developed, including the sequential parallel comparison design (SPCD) that was shown to increase the statistical power as compared to the traditional randomized trial. A linear combination of the response rate differences from two phases per the SPCD is commonly used to measure the overall treatment effect size. The traditional approach to calculate the confidence interval for the overall rate difference is based on the delta method using the variance–covariance matrix of all outcomes. As outcomes from a multinomial distribution are correlated, we suggest utilizing a constrained variance–covariance matrix in the delta method. In the observation of anti-conservative coverages from asymptotic intervals, we further propose using importance sampling to develop accurate intervals. Simulation studies show that accurate intervals have better coverage probabilities than others and the interval width of accurate intervals is similar to the interval width of others. Two real trials to treat major depressive disorder are used to illustrate the application of the proposed intervals.

Suggested Citation

  • Guogen Shan & Xinlin Lu & Yahui Zhang & Samuel S. Wu, 2024. "Confidence intervals for overall response rate difference in the sequential parallel comparison design," Statistical Papers, Springer, vol. 65(8), pages 5333-5349, October.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:8:d:10.1007_s00362-024-01606-5
    DOI: 10.1007/s00362-024-01606-5
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    References listed on IDEAS

    as
    1. Guogen Shan, 2022. "Conservative confidence intervals for the intraclass correlation coefficient for clustered binary data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(10), pages 2535-2549, July.
    2. Guogen Shan & Shawn Gerstenberger, 2017. "Fisher’s exact approach for post hoc analysis of a chi-squared test," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-12, December.
    3. Matthew T. Harrison, 2012. "Conservative hypothesis tests and confidence intervals using importance sampling," Biometrika, Biometrika Trust, vol. 99(1), pages 57-69.
    Full references (including those not matched with items on IDEAS)

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