Meta-analysis of individual- and aggregate-level data

The methodology described here was developed for a systematic review and individual participant-level meta-analysis of home safety education and the provision of safety equipment for the prevention of childhood accidents. This review had a particular emphasis on exploring whether effectiveness was related to socio-demographic characteristics previously shown to be associated with injury risk. Individual participant data were only made available to us for a proportion of the included studies. This resulted in the need for developing a new methodology to combine the available data most efficiently. Our objective was to develop a (random effects) meta-analysis model that could synthesize both individual-level and aggregate-level binary outcome data while exploring the effects of binary covariates also available in a combination of individual participant and aggregate level data. To add further complication, the studies to be combined were a mixture of cluster and individual participant-allocated designs.A Bayesian model using Markov chain Monte Carlo methods to estimate parameters is described which efficiently synthesizes the data by allowing different models to be fitted to the different study design and data format combinations available. Initially we describe a model to estimate mean effects ignoring the influence of the covariates, and then extend it to include a binary covariate. The application of the method is illustrated by application to one outcome from the motivating home safety meta-analysis for illustration. Using the same general approach, it would be possible to develop further 'tailor made' evidence synthesis models to synthesize all available evidence most effectively.