Use of Stacked Proportional Bar Graphs ("Grotta Bars") in Observational Neurology Research: A Meta-Research Study

Stacked proportional bar graphs (nicknamed "Grotta bars") are commonly used to visualize functional outcome scales in stroke research and are also used in other domains of neurology research. While lending themselves to a straightforward causal interpretation in ideal randomized controlled trials, in observational studies, Grotta bars cannot be generally interpreted causally if they show unadjusted, confounded comparisons. In a sample of recent observational neurology studies with confounding-adjusted effect estimates, we aimed to determine the frequency with which Grotta bars were used to visualize functional outcomes and how often unadjusted Grotta bars were presented without an accompanying adjusted version. We also assessed the methods used to generate adjusted Grotta bars.

We identified the 15 top-ranked clinical neurology journals, according to journal impact factor, publishing full-length original research in English. Using PubMed, we retrieved all records published in these journals between 2020 and 2021 after applying a filter for observational studies. We included and systematically examined all observational studies aiming to identify a cause-and-effect relationship with an ordinal functional outcome and confounding-adjusted effect estimate. We determined whether at least 1 comparison using Grotta bars was present, whether the visualized comparisons were adjusted, and which adjustment strategies were applied to generate these graphs.

A total of 250 studies met all inclusion criteria. Of these, 93 (37.2%) used Grotta bars to depict functional outcome scale distributions, with 76 (81.7%) presenting only Grotta bars without model-based adjustment. These bars were most commonly presented in studies with stroke patient populations; 87 of 192 studies (45.3%) presented Grotta bars. Among the 17 studies that presented Grotta bars adjusted using a model, the adjustment strategies included propensity score matching (n = 10; 58.8%), regression (n = 6; 35.3%), and inverse probability weighting (n = 1; 5.9%).

Studies that presented adjusted associations for functional outcomes commonly showed only unadjusted Grotta bars, which alone have little value for causal questions. In observational research, Grotta bars are most informative if an adjusted version, aligning with adjusted effect estimates, is presented directly alongside the unadjusted version. Based on our findings, we offer recommendations to help authors generate more informative Grotta bars and to facilitate correct interpretation for readers.