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Int J Epidemiol. 2018 Nov 9. doi: 10.1093/ije/dyy229. [Epub ahead of print]

Intervention effect estimates in cluster randomized versus individually randomized trials: a meta-epidemiological study.

Author information

1
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
2
Université de Tours, Université de Nantes, INSERM, SPHERE U1246, Tours, France.
3
INSERM CIC1415, CHRU de Tours, Tours, France.
4
Centre for Primary Care and Public Health, Queen Mary University of London, London, UK.
5
Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Département Biostatistique Santé Publique et Information Médicale, Hôpitaux Universitaires Pitié Salpêtrière - Charles Foix, AP-HP, Paris, France.

Abstract

Background:

Cluster (CRTs) and individually randomized trials (IRTs) are often pooled together in meta-analyses (MAs) of randomized trials. However, the potential systematic differences in intervention effect estimates between these two trial types has never been investigated. Therefore, we conducted a meta-epidemiological study comparing intervention effect estimates between CRTs and IRTs.

Methods:

All Cochrane MAs including at least one CRT and one IRT, published between 1 January 2010 and 31 December 2014, were included. For each MA, we estimated a ratio of odds ratios (ROR) for binary outcomes or a difference of standardized differences (DSMD) for continuous outcomes, where less than 1 (or 0, respectively) indicated a greater intervention effect estimate with CRTs.

Results:

Among 1301 screened reviews, we selected 121 MAs, of which 76 had a binary outcome and 45 had a continuous outcome. For binary outcomes, intervention effect estimates did not differ between CRTs and IRTs [ROR 1.00, 95% confidence interval (0.93 to 1.08)]. Subgroup and adjusted analyses led to consistent results. For continuous outcomes, the DSMD was 0.13 (0.06 to 0.19). It was lower for MAs with a pharmacological intervention [-0.03, (-0.12 to 0.07)], an objective outcome [0.05, (-0.08 to 0.17)] or after adjusting for trial size [0.06, (-0.01 to 0.15)].

Conclusion:

For binary outcomes, CRTs and IRTs can safely be pooled in MAs because of an absence of systematic differences between effect estimates. For continuous outcomes, the results were less clear although accounting for trial sample sizes led to a non-significant difference. More research is needed for continuous outcomes and, meanwhile, MAs should be completed with subgroup analyses (CRTs vs IRTs).

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