A forest plot displays effect sizes with confidence intervals from multiple studies in a meta-analysis. Each study is represented as a point estimate with horizontal whiskers showing the confidence interval, and a diamond at the bottom shows the pooled estimate. The plot includes a vertical reference line at the null effect (typically 0 or 1), making it easy to assess statistical significance and heterogeneity across studies.
""" anyplot.ai
forest-basic: Meta-Analysis Forest Plot
Library: pygal 3.1.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-11
"""
import os
import pygal
from pygal.style import Style
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
BRAND = "#009E73"
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK,
foreground_strong=INK,
foreground_subtle=INK_MUTED,
colors=(BRAND, BRAND, BRAND, BRAND),
title_font_size=28,
label_font_size=22,
major_label_font_size=18,
legend_font_size=16,
value_font_size=14,
stroke_width=3,
)
studies = [
("Smith 2023", -0.35, -0.72, 0.02, 10.1),
("Johnson 2023", -0.52, -0.95, -0.09, 12.5),
("Williams 2022", -0.18, -0.58, 0.22, 9.8),
("Brown 2022", -0.67, -1.15, -0.19, 10.3),
("Davis 2022", -0.41, -0.78, -0.04, 14.2),
("Miller 2021", -0.29, -0.65, 0.07, 11.7),
("Wilson 2021", -0.55, -0.98, -0.12, 9.1),
("Moore 2021", -0.38, -0.71, -0.05, 13.8),
("Taylor 2020", -0.61, -1.08, -0.14, 7.6),
("Anderson 2020", -0.44, -0.82, -0.06, 12.8),
]
pooled_effect = -0.43
pooled_ci_lower = -0.58
pooled_ci_upper = -0.28
chart = pygal.XY(
width=4800,
height=2700,
title="forest-basic · pygal · anyplot.ai",
x_title="Mean Difference (95% CI)",
style=custom_style,
show_legend=False,
stroke=False,
show_y_guides=False,
show_x_guides=True,
range=(-1.3, 0.4),
margin=120,
)
whisker_data = []
for i, (_, _, ci_low, ci_high, _) in enumerate(studies):
y_pos = len(studies) - i
whisker_data.append((ci_low, y_pos))
whisker_data.append((ci_high, y_pos))
chart.add("Studies", whisker_data, stroke=True, show_dots=False, stroke_style={"width": 6})
point_data = [(effect, len(studies) - i) for i, (_, effect, _, _, _) in enumerate(studies)]
chart.add("Effect", point_data, dots_size=14, stroke=False)
chart.add(
"Null",
[(0, -0.5), (0, len(studies) + 0.5)],
stroke=True,
show_dots=False,
stroke_style={"width": 3, "dasharray": "12, 6"},
)
diamond_half_height = 0.4
diamond_edges = [
(pooled_ci_lower, 0),
(pooled_effect, diamond_half_height),
(pooled_ci_upper, 0),
(pooled_effect, -diamond_half_height),
]
chart.add("Pooled", diamond_edges, stroke=True, show_dots=False, stroke_style={"width": 4})
y_labels = []
for i, (study, _, ci_low, ci_high, _) in enumerate(studies):
y_labels.append({"value": len(studies) - i, "label": f"{study} [{ci_low:.2f}, {ci_high:.2f}]"})
y_labels.append({"value": 0, "label": f"Pooled [{pooled_ci_lower:.2f}, {pooled_ci_upper:.2f}]"})
chart.y_labels = y_labels
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
f.write(chart.render())
Part of Meta-Analysis Forest Plot on anyplot.ai.