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Designing clinical trials for the comparison of single and multiple quantiles with right-censored data

Beatriz Farah, Olivier Bouaziz, Aurélien Latouche

Abstract

Based on the test for equality of quantiles originally introduced by Kosorok (1999), we propose new power formulas for the comparison of one quantile between two treatment groups, as well as for the comparison of a collection of quantiles. Under the null hypothesis of equality of quantiles, the test statistic follows asymptotically a normal distribution in the univariate case and a chi-squared with J degrees of freedom in the multivariate case, with J the number of quantiles compared. The variance of the test statistic depends on the estimation of the probability density function of the distribution of failure times at the quantile being tested. In order to apply the test on real data, we propose to estimate this quantity using a resampling-based method, as an alternative to Kosorok's original kernel density estimator. The whole procedure provides a practical tool for designing and analyzing data arising from clinical trials using quantiles of survival as an endpoint. Simulation studies are performed to show the appropriateness of the power formulas. We illustrate the proposed test in a phase III randomized clinical trial where the proportional hazards assumption between treatment arms does not hold.

Designing clinical trials for the comparison of single and multiple quantiles with right-censored data

Abstract

Based on the test for equality of quantiles originally introduced by Kosorok (1999), we propose new power formulas for the comparison of one quantile between two treatment groups, as well as for the comparison of a collection of quantiles. Under the null hypothesis of equality of quantiles, the test statistic follows asymptotically a normal distribution in the univariate case and a chi-squared with J degrees of freedom in the multivariate case, with J the number of quantiles compared. The variance of the test statistic depends on the estimation of the probability density function of the distribution of failure times at the quantile being tested. In order to apply the test on real data, we propose to estimate this quantity using a resampling-based method, as an alternative to Kosorok's original kernel density estimator. The whole procedure provides a practical tool for designing and analyzing data arising from clinical trials using quantiles of survival as an endpoint. Simulation studies are performed to show the appropriateness of the power formulas. We illustrate the proposed test in a phase III randomized clinical trial where the proportional hazards assumption between treatment arms does not hold.
Paper Structure (10 sections, 15 equations, 3 figures, 4 tables)

This paper contains 10 sections, 15 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Univariate test
  4. Multivariate test
  5. Illustration and results
  6. Planning a clinical trial
  7. Application of the test on data from the OAK study
  8. Application of the univariate test
  9. Application of the multivariate test
  10. Discussion

Figures (3)

  • Figure 1: Comparison of scenarios. Top row: Scenario 1 with true survival curves and power analyses. Bottom row: Scenario 2 with similar comparisons. The solid line represents the control arm and the dashed line represents the experimental arm.
  • Figure 2: Power for various differences in quantiles in scenarios 1 (on the left) and 2 (on the right).
  • Figure 3: Reconstructed Kaplan-Meier curves. The dashed lines represent the survival quantiles at probabilities 0.05, 0.1, 0.3, 0.5 and 0.7.