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Meta-analyses of randomized trials aim to summarise the effects of interventions across many patients, and can seem remote from the clinical issue of how individual patients should be treated and which patient groups will benefit the most from treatment. One method that attempts to address this point entails relating the overall effect in every trial to summaries of patient characteristics. This is called meta-regression. The interpretation of such analyses is not straightforward, however, because of a combination of confounding and other biases. Much more useful is to compare the outcomes for patient subgroups within trials and combine these results across trials. Unfortunately this method is rarely possible using published information, so analyses of individual patient data from trials are necessary. Also, although meta-analyses generally summarise an intervention's effect as a relative risk reduction, the groups of patients with the greatest absolute risk reduction have the most to gain. [ABSTRACT FROM AUTHOR]