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Research Medium #baujat#influence#heterogeneity

Baujat Plot

Each study positioned by influence on heterogeneity vs influence on pooled effect.

A free, animated baujat plot you can read here or embed on any website, from Scrollchart.

Baujat Plot

Baujat Plot: Influence and HeterogeneityStudies in the upper-right quadrant drive both heterogeneity and the pooled result0246810Contribution to Q-statistic (heterogeneity)0.00.10.20.30.40.50.6Influence on pooled estimateTypical studiesHigh Q contrib, low influenceHigh influenceon pooled resultA01B02C03D04E05F06G07H08I09J10K11L12InvestigateG07, D04: shift pooled RRby > 10% if removedBaujat et al. (2002) | Median splits: Q-contrib > 1.4, influence > 0.1 define upper-right quadrant

X: contribution to overall heterogeneity. Y: influence on pooled estimate. Studies in the upper-right both drive heterogeneity and shift the result; they deserve closer scrutiny.

Good for

  • Identifying outlier studies before submitting a systematic review, to decide whether sensitivity analysis results should be reported in the main text or a supplementary table
  • Teaching the difference between heterogeneity inflation and pooled-estimate influence, which are distinct properties that often but do not always co-occur in the same study
  • Audit workflows in living systematic reviews where new studies are added periodically and influence patterns must be re-checked with each update

Source & accuracy

This baujat plot is an editorial illustration built to represent the concept accurately. Where it shows figures, they are typical or representative values chosen to make the relationship clear, not a single underlying dataset. The diagram and its explainer are reviewed and maintained centrally, and updated over time as understanding improves.

What a Baujat plot shows

A Baujat plot is a diagnostic scatterplot used in meta-analysis to identify studies that disproportionately drive results. Each study is plotted as a point with two coordinates: the horizontal axis shows the study's contribution to overall heterogeneity (typically its contribution to the Q statistic), and the vertical axis shows its influence on the pooled effect estimate.

Studies sitting in the upper-right region both add a lot of heterogeneity and shift the combined result substantially, marking them as the most influential and worth closer inspection. It was introduced by Baujat and colleagues in 2002.

How analysts use it

The plot supports sensitivity analysis. Rather than blindly removing outliers, analysts use the Baujat plot to flag candidate studies, then check whether features such as study design, sample size, or risk of bias explain their outsized effect.

It is one of several influence diagnostics, often used alongside leave-one-out analyses and forest plots, and is available in meta-analysis software such as the metafor package in R. The goal is transparency about which studies the conclusions actually depend on.

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Reference

What this is
A free, embeddable, animated baujat plot for any website.
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Researchers & academics, Science journalists, Medical educators, Health journalists.
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