Decision Curve Analysis

When to Use

Use this skill when you need to:

• evaluate whether a binary prediction model adds clinical net benefit across threshold probabilities;
• visualize decision curves and clinical-impact curves from a clinical cohort;
• export an auditable DCA model object together with summary text and PDFs.

Typical user requests:

• "Run decision-curve analysis on this binary outcome and risk-score dataset."
• "Generate DCA and clinical-impact plots for this prediction model."
• "Compare net benefit across thresholds for this case-control cohort."

When Not to Use

Do not use this skill for:

• time-to-event or survival outcomes;
• ROC-only discrimination analysis without decision-curve outputs;
• nomogram construction or calibration-curve analysis;
• multiclass outcomes or non-binary endpoints.

When to Read External Files

Situation	File to Read	Purpose
Need algorithm details	references/algorithm.md	Statistical methods and formulas
Need to run analysis	scripts/main.R	Get the complete command
Encounter errors	references/troubleshooting.md	Find solutions
Need CLI examples	references/cli-guide.md	Parameter usage examples

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Usage

Rscript scripts/main.R \
  --data_file ./clinical_dca_data.csv \
  --outcome_col fustat \
  --predictor_col riskScore \
  --output_dir ./output/

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Arguments

Short	Long	Type	Default	Description
-d	--data_file	character	required	Clinical CSV file with row names as sample IDs
	--outcome_col	character	fustat	Binary outcome column encoded as 0/1
	--predictor_col	character	riskScore	Numeric predictor column used in the logistic DCA model
	--study_design	character	case-control	Study design: case-control or cohort
	--population_prevalence	double	0.3	Population prevalence for case-control DCA (ignored for cohort design)
	--threshold_by	double	0.01	Threshold step size; values below 0.005 significantly increase computation time
	--confidence_level	double	0.95	Confidence level passed to rmda::decision_curve()
	--population_size	integer	1000	Population size used in the clinical-impact plot
	--n_cost_benefits	integer	8	Number of cost-benefit labels in the clinical-impact plot
	--show_confidence_intervals	flag	FALSE	Show confidence intervals on the decision curve
	--standardize_net_benefit	flag	FALSE	Report standardized net benefit (sNB) instead of raw net benefit (NB)
	--decision_curve_color	character	#E64B35	Decision-curve line color
	--impact_colors	character	#E64B35,#4DBBD5	Two comma-separated colors for the clinical-impact plot
	--plot_width	double	6	PDF width in inches
	--plot_height	double	5.5	PDF height in inches
	--font_family	character	sans	PDF font family
	--plot_title	character	Decision Curve Analysis	Decision-curve plot title
	--base_cex	double	0.9	Base text-size multiplier
-o	--output_dir	character	./output/	Output directory
	--overwrite	flag	FALSE	Allow writing into a non-empty output directory
-s	--seed	integer	42	Random seed for reproducibility
-T	--timeout_seconds	integer	0	Elapsed time limit in seconds; 0 disables timeout

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Input Format

Clinical Data (--data_file)

CSV file with row names as sample IDs. The dataset must contain at least one binary outcome column and one numeric predictor column.

,fustat,riskScore,FOXP3,CD45
Patient_1,1,0.630147268229631,5.7783584300481,3.5407433709834
Patient_2,0,0.23007730941193,6.70308857663772,3.11795942819676
Patient_3,1,0.534809528754818,5.46860669585825,3.40086667402884

Requirements

• File extension must be .csv.
• Row names must be non-missing, unique sample IDs.
• outcome_col and predictor_col must exist.
• Outcome values must use 0/1 encoding. Outcome values are coerced to numeric before validation; logical TRUE/FALSE are converted to 1/0. Factor or character values will produce SKILL_INVALID_PARAMETER.
• Predictor values must be finite numeric values.
• At least 20 rows, 5 positive outcomes, and 5 negative outcomes are required.

Design note: When --study_design cohort is selected, --population_prevalence has no statistical effect; the raw observed event rate is used instead. A warning is emitted if you set a non-default population_prevalence with cohort design.

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Output Files

File	Format	Description
data/dca_model.rds	RDS	Saved rmda::decision_curve() result object
table/dca_summary.txt	Plain text	Text summary of decision-curve net benefit statistics
plot/decision_curve.pdf	PDF	Decision-curve plot
plot/clinical_impact_curve.pdf	PDF	Clinical-impact plot
session_info.txt	Plain text	Session information and run parameters

dca_summary.txt

Summary fields include:

• threshold-specific net benefit statistics from summary(dca_model);
• the selected measure (NB or sNB);
• the fitted formula and study-design context recorded in session_info.txt.

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Workflow

Step 1: Validate Input

• Confirm the clinical CSV exists and is readable.
• Check that the requested outcome and predictor columns are present.
• Validate sample IDs, binary outcome coding, and numeric predictor values.
• Emit warning if population_prevalence is non-default and study_design is cohort.

Step 2: Prepare Analysis Dataset

• Keep only the outcome and predictor columns required for DCA.
• Coerce outcome and predictor values to numeric.
• Reject cohorts with too few rows or too few events/non-events.

Step 3: Fit Decision-Curve Model

• Fit a logistic decision-curve model with rmda::decision_curve().
• Build a threshold grid from 0 to 1 using threshold_by.
• Apply population_prevalence when study_design is case-control.

Step 4: Save Outputs

• Save the fitted DCA object as .rds.
• Export the text summary as .txt.
• Render the decision curve and clinical-impact curve as PDFs.
• Record session metadata for reproducibility.

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Agent Response Contract

After a successful run, report:

1. Study design and predictor used (e.g., case-control, riskScore)
2. Net benefit metric reported (NB or sNB)
3. Threshold range and step used for the grid
4. Key finding: net benefit at clinically relevant threshold(s) from dca_summary.txt
5. Artifact paths: plot/decision_curve.pdf, plot/clinical_impact_curve.pdf, data/dca_model.rds

---

Examples

Basic Usage

Rscript scripts/main.R \
  --data_file clinical_dca_data.csv \
  --outcome_col fustat \
  --predictor_col riskScore \
  --output_dir ./output/

Cohort Design With Custom Plotting

Rscript scripts/main.R \
  --data_file clinical_dca_data.csv \
  --study_design cohort \
  --outcome_col fustat \
  --predictor_col riskScore \
  --plot_title "Cohort DCA" \
  --decision_curve_color "#3C5488" \
  --impact_colors "#3C5488,#00A087" \
  --show_confidence_intervals \
  --output_dir ./cohort_output/

With Bundled Test Data

Rscript scripts/main.R \
  --data_file tests/data/dca_data.csv \
  --outcome_col fustat \
  --predictor_col riskScore \
  --output_dir tests/output/ \
  --overwrite

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Error Handling

Error	Cause	Solution
SKILL_INVALID_PARAMETER	Invalid design, invalid numeric range, invalid outcome coding, insufficient rows/class counts, or failed model fitting	Check arguments, data ranges, and binary outcome coding
SKILL_FILE_NOT_FOUND	Input CSV does not exist	Verify the input path
SKILL_MISSING_COLUMNS	Required columns are absent	Check outcome_col and predictor_col names
SKILL_EMPTY_DATA	Input file is empty or contains no usable rows/columns	Check the CSV content
SKILL_SAMPLE_MISMATCH	Reserved for cross-file sample mismatch scenarios	Not expected for this single-file workflow
SKILL_PACKAGE_NOT_FOUND	Required R package is missing	Install with: Rscript -e "install.packages('rmda', repos='https://cloud.r-project.org')"

IF error persists, READ: references/troubleshooting.md

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Input Validation

This skill accepts: a single clinical CSV file with a binary outcome column (0/1 encoded) and a numeric predictor column, for decision curve analysis of a binary prediction model.

If the user's request does not involve decision curve analysis of a binary prediction model — for example, asking to run survival analysis, build ROC curves only, construct a nomogram, or analyze multiclass outcomes — do not proceed with the workflow. Instead respond:

"Decision Curve Analysis is designed to evaluate the clinical utility of binary prediction models by computing net benefit across decision thresholds. Your request appears to be outside this scope. Please provide a binary outcome dataset for DCA, or use a more appropriate tool for survival analysis, ROC analysis, or nomogram construction."

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Testing

Smoke Test With Included Data

Rscript scripts/main.R --help

Rscript scripts/main.R \
  --data_file tests/data/dca_data.csv \
  --outcome_col fustat \
  --predictor_col riskScore \
  --output_dir tests/output/ \
  --overwrite

Automated Smoke Test Script

Rscript tests/run_smoke_test.R

Optional shell wrapper:

bash tests/run_smoke_test.sh

Expected Output

tests/output/
|-- data/dca_model.rds
|-- plot/clinical_impact_curve.pdf
|-- plot/decision_curve.pdf
|-- session_info.txt
`-- table/dca_summary.txt

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References

1. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models.
2. rmda package documentation for clinical decision-curve analysis.

For detailed algorithm, READ: references/algorithm.md

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Implementation Checklist

• CLI parsing with optparse
• set.seed() for reproducibility
• Top-level CRAN dependency checks
• Session info recording
• Timeout parameter exposed as CLI option
• Relative-path source() usage via get_script_dir()
• Modular script structure in scripts/
• Test data provided in tests/data/
• Error handling with SKILL_* codes
• Reference docs provided in references/

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Last updated: 2026-04-27 | Version: 1.1.0