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Analysis methods for covariate-constrained cluster randomized trials with time-to-event outcomes.

Amy M Crisp1, M Elizabeth Halloran2,3, Matt D T Hitchings4

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BMC Medical Research Methodology
|January 23, 2025
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Summary

Constrained randomization in cluster randomized trials improves statistical power for time-to-event outcomes. A new permutation test offers robust control of type I error rates, outperforming model-based tests in simulations.

Keywords:
Clinical trial designCluster-randomizedConstrained randomizationPermutation testTime-to-event

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Cluster randomized trials (CRTs) often involve few clusters, necessitating efficient randomization techniques.
  • Constrained randomization balances covariates in CRTs, with prior work focusing on continuous or binary outcomes.
  • Extending covariate-adjusted analysis to time-to-event outcomes in CRTs is crucial for robust trial design.

Purpose of the Study:

  • To evaluate statistical methods for time-to-event outcomes in cluster randomized trials using constrained randomization.
  • To compare a novel permutation test against existing model-based approaches (Cox models) for analyzing time-to-event data in CRTs.
  • To assess the impact of covariate balancing on statistical power and type I error rates under different cluster numbers.

Main Methods:

  • A simulation study compared simple randomization versus constrained randomization with prognostic and non-prognostic covariates.
  • Evaluated three analysis methods: semi-parametric Cox model with robust variance, mixed-effects Cox model, and a permutation test using deviance residuals.
  • Assessed type I error rates and statistical power across varying numbers of clusters per trial arm.

Main Results:

  • The permutation test maintained nominal type I error rates, showing robustness, unlike model-based tests with few clusters.
  • All three methods performed adequately with 25 clusters per arm, as in the motivating example.
  • Constrained randomization improved power for time-to-event outcomes compared to simple randomization, with gains dependent on cluster numbers.

Conclusions:

  • Covariate-constrained randomization enhances statistical power for time-to-event outcomes in cluster randomized trials.
  • The developed permutation test is more robust against type I error inflation than model-based Cox regression approaches.
  • Adjusting for covariates in the analysis phase significantly impacts power, particularly influenced by the number of clusters per trial arm.