Univariate Multivariable Cox Regression

Univariate and Multivariable Cox Regression

When to Use

Use this skill when you need to:

• run univariate and multivariable Cox regression on a clinical survival cohort;
• identify prognostic clinical variables associated with time-to-event outcomes;
• export hazard-ratio tables and then render forest plots from those results.

Typical user requests:

• "Run single-factor and multi-factor Cox regression on this survival dataset."
• "Find prognostic variables from my clinical cohort and give me forest plots."
• "Use futime and fustat to do Cox regression for age, stage, and risk."

When Not to Use

Do not use this skill for:

• nomogram construction or calibration analysis;
• time-dependent ROC analysis or external prognostic model validation;
• feature discovery pipelines beyond the built-in univariate screening rule;
• non-survival outcomes such as binary diagnosis or differential expression.

When to Read External Files

Usage

1. Run Cox Analysis

Rscript scripts/main.R analyze \
  --data_file ./clinical_data.csv \
  --features age,gender,stage,risk \
  --time_col futime \
  --event_col fustat \
  --output_dir ./output/ \
  --seed 42

2. Generate Univariate Forest Plot

Rscript scripts/main.R forest-plot \
  --data_file ./output/table/prognosis_uni_cox_results.xlsx \
  --plot_save ./output/plot/uni_forest_plot.pdf

3. Generate Multivariable Forest Plot

Rscript scripts/main.R multi-forest-plot \
  --data_file ./output/table/prognosis_multi_cox_results.xlsx \
  --plot_save ./output/plot/multi_forest_plot.pdf

Arguments

Analyze Command

Forest Plot Commands

These arguments apply to both forest-plot and multi-forest-plot.

Input Format

Clinical Data (--data_file for analyze)

CSV file with sample IDs as row names and one column per feature/end-point variable.

",age,gender,stage,futime,fustat,risk
SAMPLE_001,65,Male,StageIII,365,1,high
SAMPLE_002,52,Female,StageII,730,0,low
SAMPLE_003,78,Male,StageIV,180,1,high

Requirements

• The file must be CSV.
• Sample IDs must be stored in the first column as row names.
• time_col must contain finite numeric values greater than 0.
• event_col must contain only 0 and 1.
• All requested features, time_col, and event_col must exist.
• At least 10 complete samples and at least 2 events are required after filtering incomplete rows.

Cox Result Table (--data_file for plot commands)

The plotting commands read the output table created by analyze.

Required columns:

• Characteristics
• Total(N)
• HR (95% CI)
• P value

Output Files

Analyze Command

Plot Commands

Result Table Columns

Workflow

Step 1: Validate and Prepare Data

• Read the clinical CSV.
• Check required columns and data types.
• Convert character predictors to factors.
• Remove rows with missing values across requested model variables.

Step 2: Run Univariate Cox Models

• Fit one Cox model per feature when --skip_univariate false.
• Export hazard ratios, confidence intervals, and p-values.

Step 3: Run Multivariable Cox Model

• Use all significant univariate features with p < 0.05.
• If fewer than 3 significant features are found, fall back to all requested features.
• Export adjusted hazard ratios, confidence intervals, and p-values.

Step 4: Generate Forest Plots

• Read the result table.
• Parse HR (95% CI) values.
• Render a one-page PDF forest plot.

Examples

Basic Analysis

Rscript scripts/main.R analyze \
  -d tests/data/sample_clinical_survival_data.csv \
  -o tests/expected_output/ \
  --overwrite

Analysis With Selected Features and Overwrite

Rscript scripts/main.R analyze \
  -d clinical_data.csv \
  -f age,gender,stage,risk \
  -o ./results/ \
  --overwrite \
  -T 600

Direct Multivariable Fit Without Univariate Screening

Rscript scripts/main.R analyze \
  -d clinical_data.csv \
  -f age,stage,risk \
  -u true \
  -o ./results/

Plot Generation

Rscript scripts/main.R forest-plot \
  -d ./results/table/prognosis_uni_cox_results.xlsx \
  -p ./results/plot/uni_forest_plot.pdf \
  -w 10 -H 7 -F 12

Rscript scripts/main.R multi-forest-plot \
  -d ./results/table/prognosis_multi_cox_results.xlsx \
  -p ./results/plot/multi_forest_plot.pdf \
  -w 10 -H 7 -F 12

Error Handling

IF error persists, READ: references/troubleshooting.md

Testing

Smoke Test With Included Data

Rscript scripts/main.R --help

Rscript scripts/main.R analyze \
  -d tests/data/sample_clinical_survival_data.csv \
  -o tests/expected_output/ \
  --overwrite

Rscript scripts/main.R forest-plot \
  -d tests/expected_output/table/prognosis_uni_cox_results.xlsx \
  -p tests/expected_output/plot/uni_forest_plot.pdf

Rscript scripts/main.R multi-forest-plot \
  -d tests/expected_output/table/prognosis_multi_cox_results.xlsx \
  -p tests/expected_output/plot/multi_forest_plot.pdf

Automated Smoke Test Script

Rscript tests/run_smoke_test.R

Optional shell wrapper:

bash tests/run_smoke_test.sh

Expected Outputs

tests/expected_output/
|-- data/analysis_data.rds
|-- plot/multi_forest_plot.pdf
|-- plot/session_info.txt
|-- plot/uni_forest_plot.pdf
|-- session_info.txt
|-- table/prognosis_multi_cox_results.xlsx
`-- table/prognosis_uni_cox_results.xlsx

References

1. Cox DR (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B.
2. Therneau TM, Grambsch PM (2000). Modeling Survival Data: Extending the Cox Model.
3. Harrell FE (2015). Regression Modeling Strategies.

For detailed algorithm, READ: references/algorithm.md

Implementation Checklist

• CLI parsing with optparse
• set.seed() for reproducibility
• requireNamespace() dependency checks
• Session info recording
• Timeout parameter exposed as CLI option
• File reading instructions in SKILL.md
• Modular script structure in scripts/
• Test data provided in tests/data/
• Error handling with SKILL_* codes
• References documented in references/

Last updated: 2026-04-16 | Version: 1.1.0