Random-effects meta-regression models for studying nonlinear dose-response relationship, with an application to alcohol and esophageal squamous cell carcinoma.

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.