This paper investigates a likelihood-based approach in meta-analysis of clinical trials involving the baseline risk as explanatory variable. The approach takes account of the errors affecting the measure of either the treatment effect or the baseline risk, while facing the potential misspecification of the baseline risk distribution. To this aim, we suggest to model the baseline risk through a flexible family of distributions represented by the skew-normal. We describe how to carry out inference within this framework and evaluate the performance of the approach through simulation. The method is compared with the routine likelihood approach based on the restrictive normality assumption for the baseline risk distribution and with the weighted least-squares regression. We apply the competing approaches to the analysis of two published datasets. Copyright © 2012 John Wiley & Sons, Ltd.