Forest Plot Creation

This skill enables creation and interpretation of forest plots, the standard visualization for meta-analysis results.

Overview

A forest plot displays individual study results alongside the pooled estimate, showing effect sizes, confidence intervals, and study weights at a glance.

When to Use This Skill

Activate this skill when users:

• Ask to "create a forest plot"
• Want to visualize meta-analysis results
• Need to interpret forest plot components
• Ask about study weights or diamonds
• Mention "blobbogram" (alternative name)

Forest Plot Anatomy

Study            Effect [95% CI]        Weight
─────────────────────────────────────────────────
Smith 2020       ──■──                   15.2%
                 0.75 [0.52, 1.08]
                 
Jones 2021         ──■──                 22.1%
                   0.82 [0.65, 1.03]
                   
Lee 2022        ─■─                      8.4%
                0.45 [0.22, 0.92]
─────────────────────────────────────────────────
Overall         ◆                        100%
                0.72 [0.58, 0.89]
                
        ←Favors Treatment | Favors Control→
                    │
                   1.0 (null)

Key Components:

• Square (■): Point estimate for each study
• Horizontal line: 95% confidence interval
• Square size: Proportional to study weight
• Diamond (◆): Pooled effect estimate
• Diamond width: Confidence interval of pooled effect
• Vertical line: Line of no effect (OR=1, SMD=0)

R Code for Forest Plots

Basic Forest Plot

library(metafor)

# Example data: effect sizes and standard errors
dat <- data.frame(
  study = c("Smith 2020", "Jones 2021", "Lee 2022", "Chen 2023"),
  yi = c(-0.29, -0.20, -0.80, -0.35),  # log odds ratios
  sei = c(0.18, 0.12, 0.37, 0.15)       # standard errors
)

# Fit random-effects model
res <- rma(yi = yi, sei = sei, data = dat, method = "REML")

# Create forest plot
forest(res, 
       slab = dat$study,
       header = "Study",
       xlab = "Log Odds Ratio",
       mlab = "Random Effects Model")

Customized Forest Plot

# Enhanced forest plot with more options
forest(res,
       slab = dat$study,
       header = c("Study", "Log OR [95% CI]"),
       xlab = "Log Odds Ratio",
       mlab = "RE Model (DL)",
       xlim = c(-2.5, 1.5),
       alim = c(-1.5, 0.5),
       at = c(-1.5, -1, -0.5, 0, 0.5),
       refline = 0,
       col = "navy",
       border = "navy",
       fonts = 1,
       cex = 0.9)

# Add text annotations
text(-2.5, -1, "Favors Treatment", pos = 4, cex = 0.8)
text(1.5, -1, "Favors Control", pos = 2, cex = 0.8)

Forest Plot with Subgroups

# Add subgroup variable
dat$subgroup <- c("RCT", "RCT", "Observational", "RCT")

# Fit model with moderator
res_sub <- rma(yi = yi, sei = sei, mods = ~ subgroup, data = dat)

# Forest plot with subgroups
forest(res_sub,
       slab = dat$study,
       order = order(dat$subgroup),
       rows = c(1:2, 5:6),
       ylim = c(-1, 9),
       header = TRUE)

# Add subgroup labels
text(-2.5, c(3, 7), c("Observational", "RCT"), font = 2, pos = 4)

Teaching Interpretation

Guide Users Through Reading

1. Start with the overall effect (diamond)

   • "The diamond shows our pooled estimate"
   • "Its position tells us the direction of effect"
   • "Its width shows our uncertainty"

2. Check if it crosses the null line

   • "Does the diamond touch or cross the vertical line?"
   • "If not, the effect is statistically significant"

3. Examine individual studies

   • "Look at how studies cluster or spread"
   • "Wide spread = heterogeneity"
   • "Larger squares = more influential studies"

4. Assess consistency

   • "Do most studies point the same direction?"
   • "Are there outliers?"

Socratic Questions for Interpretation

• "What does the size of each square tell you?"
• "Why might one study have a much wider confidence interval?"
• "If the diamond crosses 1 (or 0), what does that mean?"
• "Looking at this plot, would you say the studies agree with each other?"

Common Issues and Solutions

Assessment Questions

1. Basic: "What does a larger square in a forest plot indicate?"

   • Correct: Greater study weight (usually from larger sample/smaller variance)

2. Intermediate: "The diamond doesn't touch the line at 1. What does this tell us?"

   • Correct: The pooled effect is statistically significant

3. Advanced: "Studies show effects on both sides of the null line. What should we investigate?"

   • Correct: Heterogeneity - examine I² and consider subgroup analysis

Related Skills

• meta-analysis-fundamentals - Understanding effect sizes
• heterogeneity-analysis - When studies disagree
• r-code-generation - Advanced R programming

Adaptation Guidelines

Glass (the teaching agent) MUST adapt this content to the learner:

1. Language Detection: Detect the user's language from their messages and respond naturally in that language
2. Cultural Context: Adapt examples to local healthcare systems and research contexts when relevant
3. Technical Terms: Maintain standard English terms (e.g., "forest plot", "effect size", "I²") but explain them in the user's language
4. Level Adaptation: Adjust complexity based on user's demonstrated knowledge level
5. Socratic Method: Ask guiding questions in the detected language to promote deep understanding
6. Local Examples: When possible, reference studies or guidelines familiar to the user's region

Example Adaptations:

• 🇧🇷 Portuguese: Use Brazilian health system examples (SUS, ANVISA guidelines)
• 🇪🇸 Spanish: Reference PAHO/OPS guidelines for Latin America
• 🇨🇳 Chinese: Include examples from Chinese medical literature