Evidence-based mapping of design heterogeneity prior to meta-analysis: a systematic review and evidence synthesis.

backgroundAssessment of design heterogeneity conducted prior to meta-analysis is infrequently reported; it is often presented post hoc to explain statistical heterogeneity. However, design heterogeneity determines the mix of included studies and how they are analyzed in a meta-analysis, which in turn can importantly influence the results. The goal of this work is to introduce ways to improve the assessment and reporting of design heterogeneity prior to statistical summarization of epidemiologic studies.

methodsIn this paper, we use an assessment of sugar-sweetened beverages (SSB) and type 2 diabetes (T2D) as an example to show how a technique called 'evidence mapping' can be used to organize studies and evaluate design heterogeneity prior to meta-analysis.. Employing a systematic and reproducible approach, we evaluated the following elements across 11 selected cohort studies: variation in definitions of SSB, T2D, and co-variables, design features and population characteristics associated with specific definitions of SSB, and diversity in modeling strategies.

resultsEvidence mapping strategies effectively organized complex data and clearly depicted design heterogeneity. For example, across 11 studies of SSB and T2D, 7 measured diet only once (with 7 to 16 years of disease follow-up), 5 included primarily low SSB consumers, and 3 defined the study variable (SSB) as consumption of either sugar or artificially-sweetened beverages. This exercise also identified diversity in analysis strategies, such as adjustment for 11 to 17 co-variables and a large degree of fluctuation in SSB-T2D risk estimates depending on variables selected for multivariable models (2 to 95% change in the risk estimate from the age-adjusted model).

conclusionsMeta-analysis seeks to understand heterogeneity in addition to computing a summary risk estimate. This strategy effectively documents design heterogeneity, thus improving the practice of meta-analysis by aiding in: 1) protocol and analysis planning, 2) transparent reporting of differences in study designs, and 3) interpretation of pooled estimates. We recommend expanding the practice of meta-analysis reporting to include a table that summarizes design heterogeneity. This would provide readers with more evidence to interpret the summary risk estimates.