A model for meta-analysis of correlated binary outcomes: The case of split-body interventions

In several areas of clinical research, it is common for trials to assign different sites of the participants' bodies to different interventions. For example, a randomized controlled trial comparing surgical techniques for correcting myopia may randomize each eye of a participant to a different operation. Under such bilateral ('split-body') interventions, the observations from each participant are correlated. It is challenging to account for these correlations at the meta-analysis level, especially when the outcome is rare. Here, we present a meta-analysis model based on the bivariate binomial distribution. Our model can synthesize studies on patients who received one intervention at one body site, patients who received two interventions at different sites or a mixture of these two groups. The model can analyse studies with zero events in one or both treatment arms and can handle the case of incomplete data reporting. We use simulations to assess the performance of our model and to compare it with the bivariate beta-binomial model. In the case of bilateral interventions, our model performed well and outperformed the bivariate beta-binomial model in all scenarios explored. We illustrate our methods using two previously published meta-analyses from the fields of orthopaedics and ophthalmology. We conclude that our model constitutes a useful new tool for the meta-analysis of binary outcomes in the presence of split-body interventions.