The objective of this study is to empirically compare alternative meta-analytic methods for combining dose-response data from epidemiological studies. We identified meta-analyses of epidemiological studies that analyzed the association between a single nutrient and a dichotomous outcome. For each topic, we performed meta-analyses of odds ratios with five approaches: using extreme exposure categories only, two-step approach (first calculated study-specific effects then combined across studies) using unadjusted data, two-step approach using adjusted data, one-step approach (analyzed all data in one regression model) using unadjusted data, and one-step approach using adjusted data. Meta-analyses including only extreme exposure categories gave consistently bigger effects and wider confidence intervals than meta-analyses using all data. Confidence intervals of effect sizes were generally wider in meta-analyses with the two-step approach, compared with the one-step approach. Meta-analyses using unadjusted data and adjusted data differed, with no consistent pattern of discordance in direction, statistical significance, or magnitude of effect. We discourage using meta-analysis approaches that only use data from extreme exposure categories. The one-step approach generally has higher precision than the two-step approach. Sensitivity analysis comparing results between meta-analyses of adjusted and unadjusted data may be useful in indicating the presence of confounding.