A fundamental challenge in meta-analyses of published epidemiological dose–response data is the estimate of the function describing how the risk of disease varies across different levels of a given exposure. Issues in trend estimate include within studies variability, between studies heterogeneity, and nonlinear trend components. We present a method, based on a two-step process, that addresses simultaneously these issues. First, two-term fractional polynomial models are fitted within each study included in the meta-analysis, taking into account the correlation between the reported estimates for different exposure levels. Second, the pooled dose–response relationship is estimated considering the between studies heterogeneity, using a bivariate random-effects model. This method is illustrated by a meta-analysis aimed to estimate the shape of the dose–response curve between alcohol consumption and esophageal squamous cell carcinoma (SCC). Overall, 14 case–control studies and one cohort study, including 3000 cases of esophageal SCC, were included. The meta-analysis provided evidence that ethanol intake was related to esophageal SCC risk in a nonlinear fashion. High levels of alcohol consumption resulted in a substantial risk of esophageal SCC as compared to nondrinkers. However, a statistically significant excess risk for moderate and intermediate doses of alcohol was also observed, with no evidence of a threshold effect. Copyright © 2010 John Wiley & Sons, Ltd.