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Accounting for multiple births in randomised trials: a systematic review
Lisa Nicole Yelland1, Thomas Richard Sullivan2, Maria Makrides3
1Women's and Children's Health Research Institute, The University of Adelaide, North Adelaide, South Australia, Australia School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia.
Archives of Disease in Childhood. Fetal and Neonatal Edition
|November 13, 2014
Summary
Trials involving preterm infants often fail to account for multiple births, leading to potential issues in data analysis and clinical recommendations. Future research must address this clustering for accurate results.
Area of Science:
- Neonatal research
- Perinatal clinical trials
- Biostatistics
Background:
- Multiple births represent a significant subgroup in preterm birth research.
- Including multiple births introduces clustered data, impacting trial design and analysis.
- Current practices in preterm infant trials often overlook the unique aspects of multiple births.
Purpose of the Study:
- To evaluate how multiple births were addressed in the design and analysis of recent preterm infant trials.
- To assess the reporting of key information pertinent to multiple births in these trials.
Main Methods:
- Systematic review of multicenter randomized trials involving preterm infants (2008-2013).
- Extraction of data specifically related to the inclusion and handling of multiple births.
Main Results:
- 11% of trials excluded multiple births; 43% did not specify inclusion.
- Among trials including multiples, only 4% considered clustering in sample size and 31% in primary outcome analysis.
- 60% of trials randomizing infants failed to report randomization methods for siblings.
Conclusions:
- Reporting on multiple births in preterm infant trials is frequently inadequate.
- Clustering in multiple births is rarely accounted for, potentially leading to flawed clinical practice recommendations.
- Future neonatal and perinatal trials must incorporate clustering from multiple births in their design and analysis.
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