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L'Abbé Plot Generator for Binary Outcomes

Plot each study's treatment event rate against its control event rate to see the direction of effect, heterogeneity and the role of baseline risk.

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Pooled line
Axes
StudyEvents (treatment)No event (treatment)Events (control)No event (control)
TPT Madras 1980: control 0.6%, treatment 0.6%Comstock 1974: control 0.5%, treatment 0.4%Comstock 1976: control 0.2%, treatment 0.2%Hart & Sutherland 1977: control 1.9%, treatment 0.5%Coetzee & Berjak 1968: control 0.6%, treatment 0.4%Frimodt-Moller 1973: control 0.8%, treatment 0.7%Comstock & Webster 1969: control 0.1%, treatment 0.2%Rosenthal 1961: control 3.9%, treatment 1.0%Vandiviere 1973: control 1.6%, treatment 0.3%Stein & Aronson 1953: control 25.6%, treatment 11.7%Ferguson & Simes 1949: control 9.6%, treatment 2.0%Rosenthal 1960: control 5.0%, treatment 1.3%Aronson 1948: control 7.9%, treatment 3.3%0.10.10.20.20.50.5112255101020205050100100Control group event rate (%, log scale)Treatment group event rate (%, log scale)No differencePooled risk ratio 0.49 (random effects)Circle area reflects study size. Points below the diagonal: fewer events with treatment.

Results

Pooled risk ratio (random effects)0.49 (95% CI 0.34 to 0.70)
I²92.1%represents considerable heterogeneity (75-100%)
Studies below the diagonal11 of 13Treatment event rate lower than control
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How to Use the L'Abbé Plot Generator

  1. 1

    Enter the 2×2 counts

    Events and non-events in each group.

  2. 2

    Choose the pooled line

    Risk ratio or odds ratio.

  3. 3

    Download the plot

    PNG or SVG.

How to read a L'Abbé plot

Each circle is a study, placed by its control event rate (x-axis) and treatment event rate (y-axis) and sized by the number of participants. Studies below the diagonal had fewer events with treatment.

If studies scatter widely around the pooled-effect line, there is heterogeneity. A pattern along the x-axis suggests that the treatment effect depends on baseline risk.

How it is calculated

Sources

L'Abbé Plot Generator: FAQ

When is a L'Abbé plot useful?

For binary outcomes when you want to show heterogeneity and how effects vary with baseline risk, as a complement to the forest plot.

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