Meta-analysis involves the pooling of information across studies in order to provide both greater efficiency for estimating treatment effects and also for investigating why treatments effects may vary. By formulating a general multilevel model we can do both of these efficiently within a single-model framework, as has already been indicated and was suggested by several authors [32,33]. In addition we can combine data that are provided at either individual subject level or aggregate level or both. We shall look at a simple case but this generalizes readily [25].

Consider an underlying model for individual level data where a pair of treatments are being compared and results from a number of studies or centres are available. We write a basic model, with a continuous response Y as ytj=(xp)tj + My + uj + eij

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