Evaluating the consistency of heterogeneous results: important determinants of inconsistency in epidemiological evidence
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The examples of using sensitivity analysis to evaluate inconsistency in evidence will be presented in a poster to illustrate the value of analyzing the impact of bias and other aspects of studies that could influence the magnitude and direction of associations with health effects in epidemiological studies. The consistency of a set of results is examined via forest plots presenting effect estimates (e.g., risk ratios, odds ratios) stratified by ratings for the domains that comprise the IRIS study evaluation tool including participant selection, exposure, outcome, confounding, analysis and sensitivity. Additional factors are analyzed including exposure (low vs high) and overall study confidence. The case examples include studies from a variety of exposure settings (i.e., population-based and occupational studies) that appear to have considerable heterogeneity across studies for specific outcomes. However, when the effect estimates are stratified by exposure level and setting, and overall confidence in the exposure-outcome association, a more consistent pattern emerges.