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Empirical evaluation of meta-analytic approaches for nutrient and health outcome dose-response data.

ABSTRACT: 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.

SUBMITTER: Yu WW

PROVIDER: S-EPMC4219760 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Empirical evaluation of meta-analytic approaches for nutrient and health outcome dose-response data.

Yu Winifred W WW Schmid Christopher H CH Lichtenstein Alice H AH Lau Joseph J Trikalinos Thomas A TA

Research synthesis methods 20130901 3

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) us ...[more]

PMID: 25379059

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Project description:BackgroundA number of strategies have been proposed to handle missing binary outcome data (MOD) in systematic reviews. However, none of these have been evaluated empirically in a series of published systematic reviews.MethodsUsing published systematic reviews with network meta-analysis (NMA) from a wide range of health-related fields, we evaluated comparatively the most frequently described Bayesian modelling strategies for MOD in terms of log odds ratio (log OR), between-trial variance, inconsistency factor (i.e. difference between direct and indirect estimates for a comparison), surface under the cumulative ranking (SUCRA) and rankings. We extended the Bayesian random-effects NMA model to incorporate the informative missingness odds ratio (IMOR) parameter, and applied the node-splitting approach to investigate inconsistency locally. We considered both pattern-mixture and selection models, different structures for prior distribution of log IMOR, and different scenarios for MOD. To illustrate level of agreement between different strategies and scenarios, we used Bland-Altman plots.ResultsAddressing MOD using extreme scenarios and ignoring the uncertainty about the scenarios led to systematically different and more precise log ORs compared to modelling MOD under the missing at random (MAR) assumption. Hierarchical structure of log IMORs led to lower between-trial variance, especially in the case of substantial MOD. Assuming common-within-network or trial-specific log IMORs yielded similar posterior results for all NMA estimates, whereas intervention-specific structure systematically inflated uncertainty around log ORs and SUCRAs. Pattern-mixture model agreed with selection model, particularly under the trial-specific structure; however, selection model systematically reduced precision around log IMORs. Overall, different strategies and scenarios mostly had good agreement in the case of low MOD.ConclusionsAddressing MOD using extreme scenarios and/or ignoring the uncertainty about the scenarios may negatively affect NMA estimates. Modelling MOD via the IMOR parameter can ensure bias-adjusted estimates and offer valuable insights into missingness mechanisms. The researcher should seek an expert opinion in order to decide on the structure of log IMOR that best aligns to the condition and interventions studied and to define a proper prior distribution for log IMOR. Our findings also apply to pairwise meta-analyses.

Dataset Information

Empirical evaluation of meta-analytic approaches for nutrient and health outcome dose-response data.

Publications

Empirical evaluation of meta-analytic approaches for nutrient and health outcome dose-response data.

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