Meta Radial Plot

Workflow

above for related details. Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable. Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script. Primary implementation surface: scripts/radial_plot_backup.py . Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints. Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects. Radial Plot Explanation The radial plot (also called Radial Plot or Galbraith Plot) is a diagnostic graph for assessing heterogeneity in Meta-analysis: X-axis : Precision (Precision = 1/SE, the reciprocal of standard error) Y-axis : Standardized effect size (z = Effect / SE) Plot Elements : Scatter points : Each point represents one study Regression line : A regression line passing through the origin, with slope equal to the pooled effect size 95% confidence band : Dashed lines on both sides of the regression line, representing the 95% confidence interval Plot Interpretation : If no heterogeneity : All points should fall within the 95% confidence band, distributed along the regression line If heterogeneity present : Points will scatter outside the confidence band, deviating from the regression line High-precision studies (on the right): Have greater impact on the pooled result Studies deviating from regression line : May be sources of heterogeneity Comparison with Funnel Plot : Radial plot eliminates the effect of sample size differences through standardization Easier to identify studies inconsistent with the overall effect Symmetry is easier to judge Data Format Requirements Depending on data type, the CSV file must contain different columns: Binary (Dichotomous Data) Column Name Description study Study name group1_Events Number of events in treatment group group1_sample_size Total sample size in treatment group group2_Events Number of events in control group group2_sample_size Total sample size in control group Continuity (Continuous Data) Column Name Description study Study name group1_sample_size Sample size in treatment group group1_Mean Mean in treatment group group1_SD Standard deviation in treatment group group2_sample_size Sample size in control group group2_Mean Mean in control group group2_SD Standard deviation in control group Survival (Survival Data) Column Name Description study Study name group1_HR Hazard ratio group1_95%Lower CI 95% confidence interval lower bound group1_95%Upper CI 95% confidence interval upper bound Workflow Step 1: Validate Input Data Read the CSV file provided by the user Check necessary columns based on data type Validate data integrity (minimum 3 studies required) Step 2: Execute R Script Call the command: Rscript scripts/radial_plot.R "<csv_path>" "<type>" "<outcome_name>" "<output_dir>" Parameter description: csv_path : Absolute path to the input CSV file type : Data type (Binary / Continuity / Survival) outcome_name : Outcome indicator name (optional) output_dir : Output directory (optional) Step 3: Output Results On success, output : ═══════════════════════════════════════════ Radial Plot Generation Complete ═══════════════════════════════════════════

【Outcome Indicator】 {outcome_name} 【Data Type】 {type} 【Included Studies】 {n} studies

【Heterogeneity Statistics】 • I² = {I2}% • Tau² = {tau2} • Q = {Q}, df = {df}, P = {pval_Q}

【Pooled Effect Size】 • {effect_name} = {value} [{lower}; {upper}]

【Output Files】 • Radial Plot: {output_dir}/{type}radial{outcome}.png • Data Table: {output_dir}/{type}radial{outcome}.csv

【Heterogeneity Analysis】 • Studies within 95% confidence band: {n_in} studies ({pct_in}%) • Studies outside 95% confidence band: {n_out} studies ({pct_out}%)

【Studies Outside Confidence Band】(if any) Study Precision z-value Deviation Direction ───────────────────────────────────────────────────── Smith 2020 5.23 2.85 Above ...

【Conclusion】 {Heterogeneity assessment based on analysis results}

═══════════════════════════════════════════ R Script Dependencies The following R packages need to be installed: meta metafor ggplot2 ggrepel (optional, for label positioning to avoid overlaps) If the user's environment is missing these packages, prompt them to run: install.packages ( c ( "meta" , "metafor" , "ggplot2" , "ggrepel" ) ) When Not to Use Do not use this skill when the required source data, identifiers, files, or credentials are missing. Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions. Do not use this skill when a simpler direct answer is more appropriate than the documented workflow. Required Inputs A clearly specified task goal aligned with the documented scope. All required files, identifiers, parameters, or environment variables before execution. Any domain constraints, formatting requirements, and expected output destination if applicable. Output Contract Return a structured deliverable that is directly usable without reformatting. If a file is produced, prefer a deterministic output name such as meta_radial_plot_result.md unless the skill documentation defines a better convention. Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations. Validation and Safety Rules Validate required inputs before execution and stop early when mandatory fields or files are missing. Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material. Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result. Keep the output safe, reproducible, and within the documented scope at all times. Failure Handling If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required. If an external dependency or script fails, surface the command path, likely cause, and the next recovery step. If partial output is returned, label it clearly and identify which checks could not be completed. Quick Validation Run this minimal verification path before full execution when possible: python scripts/radial_plot_backup.py -- help Expected output format: Result file: meta_radial_plot_result.md Validation summary: PASS/FAIL with brief notes Assumptions: explicit list if any