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univariate-multivariable-cox-regression Use when running prognostic survival analysis on a clinical cohort with time-to-event data to estimate univariate and multivariable Cox proportional hazards models, export result tables, and generate forest plots. NOT for: nomogram construction, calibration curves, time-dependent ROC analysis, or model training/feature selection beyond the built-in univariate screening rule.
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下载 Zip 下载中... eval_report_univariate-multivariable-cox-regression_result.json 12.3 KB name univariate-multivariable-cox-regression description Use when running prognostic survival analysis on a clinical cohort with time-to-event data to estimate univariate and multivariable Cox proportional hazards models, export result tables, and generate forest plots. NOT for: nomogram construction, calibration curves, time-dependent ROC analysis, or model training/feature selection beyond the built-in univariate screening rule.
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
Situation File to Read Purpose Need algorithm details references/algorithm.mdStatistical workflow, assumptions, and feature-selection rule Need to run analysis scripts/main.R
Execute Rscript scripts/main.R <command> [options]
Encounter errors references/troubleshooting.mdCommon SKILL_* errors and fixes
Need CLI examples references/cli-guide.mdCommand-specific argument examples
Need test data tests/data/Minimal runnable cohort for smoke testing
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 Short Long Type Default Description -d--data_filecharacter required Clinical CSV file with sample IDs as row names -f--featurescharacter age,gender,stage,Tstage,Nstage,Mstage,riskComma-separated features for Cox analysis -t--time_colcharacter futimeSurvival time column -e--event_colcharacter fustatEvent column encoded as 1=event, 0=censored -u--skip_univariatecharacter falseSkip univariate screening and fit multivariable model on all requested features -o--output_dircharacter ./output/Output directory --overwriteflag FALSEAllow writing into a non-empty output directory -s--seedinteger 42Random seed for reproducibility -T--timeout_secondsinteger 0Elapsed time limit in seconds; 0 disables timeout
Forest Plot Commands These arguments apply to both forest-plot and multi-forest-plot.
Short Long Type Default Description -d--data_filecharacter required Cox result table in .xlsx, .xls, or .csv format -p--plot_savecharacter required Output PDF path -w--widthdouble 8Plot width in inches -H--heightdouble 6Plot height in inches -F--font_sizedouble 11Font size for forest-plot labels -s--seedinteger 42Random seed for reproducibility -T--timeout_secondsinteger 0Elapsed time limit in seconds; 0 disables timeout
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
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.
CharacteristicsTotal(N)HR (95% CI)P value
Output Files
Analyze Command File Description table/prognosis_uni_cox_results.xlsxUnivariate Cox result table. Present unless --skip_univariate true table/prognosis_multi_cox_results.xlsxMultivariable Cox result table data/analysis_data.rdsSerialized complete-case dataset used for Cox fitting session_info.txtSession info and recorded run parameters
Plot Commands File Description plot/uni_forest_plot.pdfPDF forest plot generated by forest-plot plot/multi_forest_plot.pdfPDF forest plot generated by multi-forest-plot plot/session_info.txtSession info and plotting parameters written beside the PDF
Result Table Columns Column Description CharacteristicsVariable name for continuous terms, or level label for categorical terms Total(N)Number of complete-case samples used for modeling HR (95% CI)Hazard ratio with 95% confidence interval P valueWald-test p-value formatted to three decimals or <0.001 featureSource feature corresponding to each row
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 Error Cause Solution SKILL_INVALID_PARAMETERMissing required CLI value, invalid extension, unknown command, unreadable CSV input, invalid clinical values, too few complete samples/events, or Cox model fitting failure caused by unsupported input data Check argument names, file types, clinical value constraints, and model input suitability SKILL_FILE_NOT_FOUNDInput file path does not exist Verify the input path SKILL_MISSING_COLUMNSRequired columns are absent from the clinical file or plot table Check column names and spelling SKILL_EMPTY_DATAInput file or plot table contains no usable rows Verify file content and export process SKILL_PACKAGE_NOT_FOUNDRequired R package is missing Install the listed CRAN package(s)
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
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
Cox DR (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B .
Therneau TM, Grambsch PM (2000). Modeling Survival Data: Extending the Cox Model .
Harrell FE (2015). Regression Modeling Strategies .
For detailed algorithm , READ: references/algorithm.md
Implementation Checklist
Last updated: 2026-04-16 | Version: 1.1.0
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