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This study introduces new statistical methods for analyzing cluster-randomized trials (CRTs) with survival data. The doubly robust estimators accurately estimate treatment effects at both cluster and individual levels, even with complex censoring.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Cluster-randomized trials (CRTs) involve randomizing groups, necessitating specialized analysis.
  • Estimating treatment effects in CRTs requires differentiating cluster-level and individual-level impacts.
  • Survival outcomes in CRTs present unique analytical challenges, especially with right-censoring.

Purpose of the Study:

  • To formally define cluster-level and individual-level treatment effect estimands for CRTs with right-censored survival data.
  • To propose novel, doubly robust estimators for these estimands.
  • To provide robust statistical methods for analyzing survival outcomes in CRTs.

Main Methods:

  • Developed doubly robust estimators for cluster- and individual-level treatment effects.
  • Addressed dependent censoring on baseline covariates, ensuring consistency if either outcome or censoring model is correct.
  • Employed various modeling strategies for censoring and outcome distributions.
  • Utilized a deletion-based jackknife method for variance and interval estimation.

Main Results:

  • Proposed estimators demonstrated consistency under dependent censoring.
  • Simulation studies confirmed adequate finite sample performance of the methods.
  • The methods were successfully applied to a real-world CRT with survival endpoints.

Conclusions:

  • The developed doubly robust estimators provide a reliable approach for analyzing survival data in CRTs.
  • These methods enhance causal inference by accurately distinguishing treatment effects at different levels.
  • The findings offer valuable tools for researchers conducting CRTs with survival outcomes.