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Statistical analysis and handling of missing data in cluster randomised trials: protocol for a systematic review
Mallorie Fiero1, Shuang Huang1, Melanie L Bell1
1Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA.
BMJ Open
|May 15, 2015
Summary
This review evaluates statistical methods for handling missing outcome data in cluster randomized trials (CRTs). It highlights the importance of appropriate analysis to minimize bias and maintain statistical power in health research.
Area of Science:
- Health Research Methodology
- Biostatistics
- Clinical Trials
Background:
- Cluster randomized trials (CRTs) are essential for health research when individual randomization is impractical or risks treatment contamination.
- Missing outcome data in CRTs can significantly reduce statistical power and introduce bias.
- Evaluating methods for statistical analysis and missing data handling is crucial for the integrity of CRT findings.
Purpose of the Study:
- To systematically review and evaluate statistical analysis methods for handling missing outcome data in cluster randomized trials.
- To identify and assess common approaches used in CRTs to address missing data concerning primary outcomes.
Main Methods:
- A systematic literature search of CRTs published between August 2013 and July 2014 was conducted across PubMed, Web of Science, and PsycINFO.
- A random sample of 86 studies was selected for review, with data extraction performed by two independent reviewers using a standardized template.
- Descriptive statistics will be employed to summarize the findings regarding statistical analysis and missing data handling in the selected CRTs.
Main Results:
- (Results will be presented after data analysis)
Conclusions:
- (Conclusions will be drawn after data analysis and dissemination)
