Article Text

Statistical analysis and handling of missing data in cluster randomised trials: protocol for a systematic review
  1. Mallorie Fiero,
  2. Shuang Huang,
  3. Melanie L Bell
  1. Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA
  1. Correspondence to Mallorie Fiero; mfiero{at}


Introduction Cluster randomised trials (CRTs) randomise participants in groups, rather than as individuals, and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomisation is not feasible. Missing outcome data can reduce power in trials, including in CRTs, and is a potential source of bias. The current review focuses on evaluating methods used in statistical analysis and handling of missing data with respect to the primary outcome in CRTs.

Methods and analysis We will search for CRTs published between August 2013 and July 2014 using PubMed, Web of Science and PsycINFO. We will identify relevant studies by screening titles and abstracts, and examining full-text articles based on our predefined study inclusion criteria. 86 studies will be randomly chosen to be included in our review. Two independent reviewers will collect data from each study using a standardised, prepiloted data extraction template. Our findings will be summarised and presented using descriptive statistics.

Ethics and dissemination This methodological systematic review does not need ethical approval because there are no data used in our study that are linked to individual patient data. After completion of this systematic review, data will be immediately analysed, and findings will be disseminated through a peer-reviewed publication and conference presentation.

  • Missing data
  • Dropout
  • Cluster randomized trials
  • Bias

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:

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