name: meta-analysis version: 1.0.0 description: Complete meta-analysis generation for neurosurgical outcomes author: NeuroResearch Agent license: MIT

Activation triggers - patterns that activate this skill

triggers:

pattern: "meta-analysis"
pattern: "forest plot"
pattern: "funnel plot"
pattern: "heterogeneity"
pattern: "pool.*studies"
pattern: "synthesize.*data"
pattern: "publication bias"
pattern: "subgroup analysis"
pattern: "sensitivity analysis"
pattern: "I-squared|I²"

Required MCP servers

requires:

r-execute
filesystem

Tools provided by this skill

tools:

name: analyze_binary description: Meta-analysis of binary outcomes (OR, RR, RD) script: scripts/binary_meta.R
name: analyze_continuous description: Meta-analysis of continuous outcomes (MD, SMD) script: scripts/continuous_meta.R
name: analyze_proportion description: Meta-analysis of proportions (single-arm studies) script: scripts/proportion_meta.R
name: analyze_survival description: Meta-analysis of hazard ratios script: scripts/survival_meta.R
name: generate_forest description: Generate publication-quality forest plot script: scripts/forest_plot.R
name: assess_heterogeneity description: Comprehensive heterogeneity assessment script: scripts/heterogeneity.R
name: assess_publication_bias description: Publication bias assessment script: scripts/publication_bias.R
name: run_subgroup description: Subgroup meta-analysis script: scripts/subgroup.R
name: run_sensitivity description: Sensitivity analyses (leave-one-out, influence) script: scripts/sensitivity.R

Data schemas

schemas: binary_input: required: - study: string - events_int: integer - n_int: integer - events_ctrl: integer - n_ctrl: integer optional: - year: integer - subgroup: string

continuous_input: required: - study: string - n_int: integer - mean_int: number - sd_int: number - n_ctrl: integer - mean_ctrl: number - sd_ctrl: number optional: - year: integer - subgroup: string

proportion_input: required: - study: string - events: integer - total: integer optional: - year: integer - subgroup: string

Output specifications

outputs:

figures/forest_*.png
figures/funnel_*.png
results/meta_summary.csv
results/heterogeneity.txt
results/publication_bias.txt

Meta-Analysis Skill

Overview

This skill provides comprehensive meta-analysis capabilities for neurosurgical systematic reviews. It supports binary outcomes (mortality, complications), continuous outcomes (scores, measurements), proportions (single-arm success rates), and survival data (hazard ratios).

When to Use

Activate this skill when the user:

Has extracted data ready for pooling
Requests forest plots, funnel plots, or heterogeneity assessment
Needs subgroup or sensitivity analyses
Asks about publication bias or effect sizes
Wants to synthesize results from multiple studies

Workflow

1. Data Preparation

Before analysis, ensure data is in the correct format:

# Binary outcomes (2x2 data)
study,year,events_int,n_int,events_ctrl,n_ctrl,subgroup
Smith,2020,10,50,5,50,early
Jones,2021,15,60,8,55,late

# Continuous outcomes
study,year,n_int,mean_int,sd_int,n_ctrl,mean_ctrl,sd_ctrl
Smith,2020,50,25.3,5.2,50,30.1,6.1

# Proportions (single-arm)
study,year,events,total
Smith,2020,42,50
Jones,2021,38,55

2. Main Analysis

For binary outcomes (OR, RR, RD):

Run a meta-analysis of mortality using OR with random effects.
Input: extractions/pooled_data.csv

For continuous outcomes (MD, SMD):

Run a meta-analysis of ODI scores using MD.
Input: extractions/continuous_data.csv

For proportions:

Calculate the pooled proportion of surgical success.
Input: extractions/case_series_data.csv

3. Interpretation Guidelines

Effect Measures

OR (Odds Ratio): >1 favors control, <1 favors intervention
RR (Risk Ratio): Same interpretation as OR
MD (Mean Difference): Actual units, clinically interpretable
SMD (Standardized Mean Difference): 0.2 small, 0.5 medium, 0.8 large

Heterogeneity (I²)

0-25%: Low heterogeneity
25-50%: Moderate heterogeneity
50-75%: Substantial heterogeneity
75%: Considerable heterogeneity

When I² > 50%, consider:

Exploring sources via subgroup/meta-regression
Using random-effects model (default)
Reporting prediction interval
Conducting sensitivity analyses

Publication Bias

Funnel plot asymmetry suggests bias
Egger's test: p < 0.10 indicates asymmetry
Peters' test: Preferred for binary outcomes
Trim-and-fill: Estimates adjusted effect

4. Reporting Checklist

For each meta-analysis, generate:

Forest plot with prediction interval
Summary statistics (effect, CI, p-value)
Heterogeneity statistics (I², τ², Q, prediction interval)
Publication bias assessment (if k ≥ 10)
Sensitivity analyses
Subgroup analyses (if pre-specified)

R Code Templates

Binary Outcomes Template

library(meta)
library(metafor)

data <- read.csv("{{INPUT}}")

ma <- metabin(
  event.e = events_int,
  n.e = n_int,
  event.c = events_ctrl,
  n.c = n_ctrl,
  studlab = paste(study, year),
  data = data,
  sm = "{{MEASURE}}",  # OR, RR, RD
  method = "MH",        # MH, Inverse, GLMM
  random = TRUE,
  fixed = FALSE,
  prediction = TRUE,
  hakn = TRUE           # Knapp-Hartung adjustment
)

# Forest plot
png("{{OUTPUT}}/forest_plot.png", width = 1200, height = 800, res = 150)
forest(ma,
       sortvar = TE,
       prediction = TRUE,
       print.tau2 = TRUE,
       print.I2 = TRUE,
       leftcols = c("studlab", "event.e", "n.e", "event.c", "n.c"),
       rightcols = c("effect", "ci", "w.random"))
dev.off()

Heterogeneity Assessment

# Comprehensive heterogeneity assessment
cat("=== HETEROGENEITY ASSESSMENT ===\n\n")

# Statistics
cat(sprintf("I² = %.1f%% [%.1f%%, %.1f%%]\n",
            ma$I2*100, ma$lower.I2*100, ma$upper.I2*100))
cat(sprintf("τ² = %.4f (τ = %.4f)\n", ma$tau2, sqrt(ma$tau2)))
cat(sprintf("H² = %.2f\n", ma$H^2))
cat(sprintf("Q = %.2f, df = %d, p = %.4f\n", ma$Q, ma$df.Q, ma$pval.Q))
cat(sprintf("\nPrediction interval: [%.2f, %.2f]\n",
            exp(ma$lower.predict), exp(ma$upper.predict)))

# Baujat plot (outlier detection)
png("{{OUTPUT}}/baujat_plot.png", width = 800, height = 600, res = 150)
baujat(ma)
dev.off()

# GOSH plot (if < 15 studies)
if (ma$k <= 15) {
  gosh_result <- gosh(ma)
  png("{{OUTPUT}}/gosh_plot.png", width = 800, height = 600, res = 150)
  plot(gosh_result)
  dev.off()
}

Publication Bias Assessment

# Only reliable if k >= 10
if (ma$k >= 10) {
  # Funnel plot
  png("{{OUTPUT}}/funnel_plot.png", width = 800, height = 600, res = 150)
  funnel(ma, studlab = TRUE, cex.studlab = 0.7)
  dev.off()
  
  # Contour-enhanced funnel plot
  png("{{OUTPUT}}/funnel_contour.png", width = 800, height = 600, res = 150)
  funnel(ma, contour = c(0.9, 0.95, 0.99),
         col.contour = c("darkgray", "gray", "lightgray"))
  legend("topright", c("p < 0.10", "p < 0.05", "p < 0.01"),
         fill = c("darkgray", "gray", "lightgray"), bty = "n")
  dev.off()
  
  # Statistical tests
  cat("\n=== PUBLICATION BIAS TESTS ===\n\n")
  
  # Egger's test (continuous outcomes)
  egger <- metabias(ma, method = "Egger")
  cat(sprintf("Egger's test: t = %.2f, p = %.4f\n",
              egger$statistic, egger$p.value))
  
  # Peters' test (binary outcomes - preferred)
  peters <- metabias(ma, method = "Peters")
  cat(sprintf("Peters' test: t = %.2f, p = %.4f\n",
              peters$statistic, peters$p.value))
  
  # Trim-and-fill
  tf <- trimfill(ma)
  cat(sprintf("\nTrim-and-fill: %d studies imputed\n", tf$k0))
  cat(sprintf("Adjusted estimate: %.2f [%.2f, %.2f]\n",
              exp(tf$TE.random), exp(tf$lower.random), exp(tf$upper.random)))
  
  # Funnel with imputed studies
  png("{{OUTPUT}}/funnel_trimfill.png", width = 800, height = 600, res = 150)
  funnel(tf, studlab = TRUE)
  dev.off()
  
} else {
  cat("\nNote: k < 10, publication bias tests unreliable.\n")
  cat("Visual inspection of funnel plot only.\n")
}

Sensitivity Analyses

# Leave-one-out analysis
l1o <- metainf(ma, pooled = "random")
png("{{OUTPUT}}/leave_one_out.png", width = 1000, height = 600, res = 150)
forest(l1o)
dev.off()

# Influence diagnostics
inf <- influence(ma)
png("{{OUTPUT}}/influence.png", width = 1000, height = 800, res = 150)
plot(inf)
dev.off()

# Cumulative meta-analysis (by year)
cum <- metacum(ma, sortvar = data$year)
png("{{OUTPUT}}/cumulative.png", width = 1000, height = 600, res = 150)
forest(cum)
dev.off()

# Report influential studies
cat("\n=== INFLUENTIAL STUDIES ===\n\n")
influential <- which(inf$is.infl)
if (length(influential) > 0) {
  cat("The following studies are potentially influential:\n")
  for (i in influential) {
    cat(sprintf("  - %s\n", ma$studlab[i]))
  }
} else {
  cat("No studies identified as influential.\n")
}

Subgroup Analysis

# Update with subgroup
ma_sub <- update(ma, subgroup = data${{SUBGROUP}})

# Forest plot with subgroups
png("{{OUTPUT}}/forest_subgroup.png", width = 1400, height = 1000, res = 150)
forest(ma_sub,
       sortvar = TE,
       prediction = TRUE,
       subgroup = TRUE,
       print.subgroup.labels = TRUE,
       subgroup.hetstat = TRUE)
dev.off()

# Test for subgroup differences
cat("\n=== SUBGROUP ANALYSIS ===\n\n")
cat(sprintf("Test for subgroup differences:\n"))
cat(sprintf("  Q = %.2f, df = %d, p = %.4f\n",
            ma_sub$Q.b.random, ma_sub$df.Q.b, ma_sub$pval.Q.b.random))

# Within-subgroup heterogeneity
for (sg in unique(data${{SUBGROUP}})) {
  ma_sg <- subset(ma_sub, subgroup == sg)
  cat(sprintf("\n%s (k = %d):\n", sg, ma_sg$k))
  cat(sprintf("  Effect: %.2f [%.2f, %.2f]\n",
              exp(ma_sg$TE.random), exp(ma_sg$lower.random), exp(ma_sg$upper.random)))
  cat(sprintf("  I² = %.1f%%\n", ma_sg$I2 * 100))
}

Common Issues & Solutions

Missing SDs

If SDs are not reported, estimate from:

SE: SD = SE × √n
95% CI: SD = (upper - lower) × √n / 3.92
IQR: SD ≈ IQR / 1.35
Range: SD ≈ Range / 4

Zero Events

Add continuity correction:

metabin(..., incr = 0.5, method.incr = "only0")

Median/IQR Data

Convert using Wan et al. (2014) method:

library(estmeansd)
bc.mean.sd(q1.val = Q1, med.val = median, q3.val = Q3, n = n)

Hazard Ratios

If only HR and CI reported:

log_hr <- log(hr)
se <- (log(ci_upper) - log(ci_lower)) / 3.92

GRADE Integration

After analysis, assess certainty:

Risk of bias: Overall from RoB assessment
Inconsistency: Based on I² and prediction interval
Indirectness: Population, intervention, outcome match
Imprecision: CI crosses clinical threshold or wide
Publication bias: Funnel asymmetry, trim-and-fill impact

Generate Summary of Findings table with absolute effects.

meta-analysis

SKILL.md