Get access

Sample size in cluster-randomized trials with time to event as the primary endpoint

Authors

  • Antje Jahn-Eimermacher,

    Corresponding author
    1. Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Medical Center of the Johannes Gutenberg-University, 55131 Mainz, Germany
    • Correspondence to: Antje Jahn-Eimermacher, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Medical Center of the Johannes Gutenberg-University, Langenbeckstr.1, 55131 Mainz, Germany.

      E-mail: antje.jahn@unimedizin-mainz.de

    Search for more papers by this author
  • Katharina Ingel,

    1. Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Medical Center of the Johannes Gutenberg-University, 55131 Mainz, Germany
    Search for more papers by this author
  • Astrid Schneider

    1. Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Medical Center of the Johannes Gutenberg-University, 55131 Mainz, Germany
    Search for more papers by this author

Abstract

In cluster-randomized trials, groups of individuals (clusters) are randomized to the treatments or interventions to be compared. In many of those trials, the primary objective is to compare the time for an event to occur between randomized groups, and the shared frailty model well fits clustered time-to-event data. Members of the same cluster tend to be more similar than members of different clusters, causing correlations. As correlations affect the power of a trial to detect intervention effects, the clustered design has to be considered in planning the sample size. In this publication, we derive a sample size formula for clustered time-to-event data with constant marginal baseline hazards and correlation within clusters induced by a shared frailty term. The sample size formula is easy to apply and can be interpreted as an extension of the widely used Schoenfeld's formula, accounting for the clustered design of the trial. Simulations confirm the validity of the formula and its use also for non-constant marginal baseline hazards. Findings are illustrated on a cluster-randomized trial investigating methods of disseminating quality improvement to addiction treatment centers in the USA. Copyright © 2012 John Wiley & Sons, Ltd.

Ancillary