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Beyond bar and line graphs: time for a new data presentation paradigm.

ABSTRACT: Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

SUBMITTER: Weissgerber TL

PROVIDER: S-EPMC4406565 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Beyond bar and line graphs: time for a new data presentation paradigm.

Weissgerber Tracey L TL Milic Natasa M NM Winham Stacey J SJ Garovic Vesna D VD

PLoS biology 20150422 4

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data ...[more]

PMID: 25901488

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Project description:BackgroundModified Rankin Scale (mRS) scores are used to measure functional outcomes after stroke. Researchers create horizontal stacked bar graphs (nicknamed "Grotta bars") to illustrate distributional differences in scores between groups. In well-conducted randomized controlled trials, Grotta bars have a causal interpretation. However, the common practice of exclusively presenting unadjusted Grotta bars in observational studies can be misleading in the presence of confounding. We demonstrated this problem and a possible solution using an empirical comparison of 3-month mRS scores among stroke/TIA patients discharged home versus elsewhere after hospitalization.Patients and methodsUsing data from the Berlin-based B-SPATIAL registry, we estimated the probability of being discharged home conditional on prespecified measured confounding factors and generated stabilized inverse probability of treatment (IPT) weights for each patient. We visualized mRS distributions by group with Grotta bars for the IPT-weighted population in which measured confounding was removed. We then used ordinal logistic regression to quantify unadjusted and adjusted associations between being discharged home and the 3-month mRS score.ResultsOf 3184 eligible patients, 2537 (79.7%) were discharged home. In the unadjusted analyses, those discharged home had considerably lower mRS compared with patients discharged elsewhere (common odds ratio, cOR = 0.13, 95% CI: 0.11-0.15). After removing measured confounding, we obtained substantially different mRS distributions, visually apparent in the adjusted Grotta bars. No statistically significant association was found after confounding adjustment (cOR = 0.82, 95% CI: 0.60-1.12).Discussion and conclusionThe practice of presenting only unadjusted stacked bar graphs for mRS scores together with adjusted effect estimates in observational studies can be misleading. IPT weighting can be implemented to create Grotta bars that account for measured confounding, which are more consistent with the presentation of adjusted results in observational studies.

Dataset Information

Beyond bar and line graphs: time for a new data presentation paradigm.

Publications

Beyond bar and line graphs: time for a new data presentation paradigm.

Similar Datasets

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