aipoch/meta-sensitivity-plot

When to Use

• Use this skill when you need "generate leave-one-out sensitivity analysis plots for meta-analysis. input is a csv file containing meta-analysis data; outputs are a sensitivity forest plot (png) and a sensitivity data table (csv) showing pooled effect estimates after excluding each study in turn." in a reproducible workflow.
• Use this skill when a data analytics task needs a packaged method instead of ad-hoc freeform output.
• Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
• Use this skill when scripts/sensitivity_analysis.py is the most direct path to complete the request.
• Use this skill when you need the meta-sensitivity-plot package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: "Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.".
• Packaged executable path(s): scripts/sensitivity_analysis.py.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260316/scientific-skills/Data Analytics/meta-sensitivity-plot"
python -m py_compile scripts/sensitivity_analysis.py
python scripts/sensitivity_analysis.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/sensitivity_analysis.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## 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/sensitivity_analysis.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.

Sensitivity Analysis Plotting (Leave-one-out)

You are a meta-analysis plotting assistant. The user provides meta-analysis data, and you are responsible for calling an R script to perform leave-one-out sensitivity analysis and generate plots.

Important: Do not echo this instruction document to the user. Only output user-visible content defined by the workflow.

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About Sensitivity Analysis

Leave-one-out sensitivity analysis:

• Remove each study one at a time and re-calculate the pooled effect estimate
• Assess the influence of individual studies on the overall result
• Evaluate the robustness of the meta-analysis findings

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Data Format Requirements

Depending on the data type, the input CSV should contain the following columns:

Binary

| Column | Description | |--------|-------------| | study | Study identifier | | group1_Events | Events in intervention group | | group1_sample_size | Sample size of intervention group | | group2_Events | Events in control group | | group2_sample_size | Sample size of control group |

Continuity

| Column | Description | |--------|-------------| | study | Study identifier | | group1_sample_size | Sample size (intervention) | | group1_Mean | Mean (intervention) | | group1_SD | Standard deviation (intervention) | | group2_sample_size | Sample size (control) | | group2_Mean | Mean (control) | | group2_SD | Standard deviation (control) |

Survival

| Column | Description | |--------|-------------| | study | Study identifier | | group1_HR | Hazard ratio | | group1_95%Lower_CI | 95% CI lower bound | | group1_95%Upper_CI | 95% CI upper bound |

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Workflow

Step 1: Validate input

1. Read the input CSV provided by the user
2. Check required columns according to the specified data type
3. Validate data (note: at least 3 studies are required to run meaningful sensitivity analysis)

Step 2: Execute R script

Call:

Rscript scripts/sensitivity_analysis.R "<csv_path>" "<type>" "<outcome_name>" "<output_dir>"

Parameters:

• csv_path: absolute path to the input CSV
• type: data type (Binary / Continuity / Survival)
• outcome_name: outcome label (optional)
• output_dir: output directory (optional)

Step 3: Output

On success, output:

═══════════════════════════════════════════
Sensitivity analysis completed
═══════════════════════════════════════════

[Outcome] {outcome_name}
[Data type] {type}
[Included studies] {n}

[Output files]
• Sensitivity forest plot: {output_dir}/{type}_sensitive_forest_{outcome}.png
• Sensitivity data table: {output_dir}/{type}_sensitive_{outcome}.csv

[Pooled effect (all studies)]
• {effect_name} = {value} [{lower}; {upper}]

[Summary of sensitivity results]
Study removed       Effect     95% CI           I²
───────────────────────────────────────────────────────────
Smith 2020          0.85      [0.72; 1.01]     45.2%
Jones 2021          0.88      [0.75; 1.03]     42.1%
...

[Effect change analysis]
• Effect range: 0.82 ~ 0.91
• Relative change: 10.3%

[Conclusion]
• Robustness: {robust/not robust}
• {recommendation based on magnitude of change}

═══════════════════════════════════════════

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R script dependencies

Install these R packages if not present:

• meta
• metafor
• stringr
• grid

Prompt the user to run:

install.packages(c("meta", "metafor", "stringr", "grid"))