AbstractIn this paper, we develop meta‐analysis models that synthesize a binary outcome from health‐care studies while accounting for participant‐level covariates. In particular, we show how to synthesize the observed event‐risk across studies while accounting for the within‐study association between participant‐level covariates and individual event probability. The models are adapted for situations where studies provide individual participant data (IPD), or a mixture of IPD and aggregate data. We show that the availability of IPD is crucial in at least some studies; this allows one to model potentially complex within‐study associations and separate them from across‐study associations, so as to account for potential ecological bias and study‐level confounding. The models can produce pertinent population‐level and individual‐level results, such as the pooled event‐risk and the covariate‐specific event probability for an individual. Application is made to 14 studies of traumatic brain injury, where IPD are available for four studies and the six‐month mortality risk is synthesized in relation to individual age. The results show that as individual age increases the probability of six‐month mortality also increases; further, the models reveal clear evidence of ecological bias, with the mean age in each study additionally influencing an individual's mortality probability. Copyright © 2010 John Wiley & Sons, Ltd.