MedCalc
by ProCodersUpdated May 4, 2026
Provides API access to MedCalc's statistical functions for biomedical research, including ROC curve analysis, survival curves, method comparisons, and meta-analysis. Clinical researchers, biostatisticians, and healthcare developers use it to compute statistics programmatically in analysis pipelines and health apps.
healthcare
statistics
biomedical-analysis
|Overview
MedCalc MCP server (medcalc-mcp) exposes MedCalc's statistical computation capabilities via API for medical data analysis. It supports common biomedical statistics without requiring the desktop software installation.
Key Capabilities
- roc_analysis: Calculates receiver operating characteristic curves, AUC values, and optimal cutoffs for diagnostic tests.
- survival_analysis: Generates Kaplan-Meier curves, log-rank tests, and Cox proportional hazards models.
- method_comparison: Produces Bland-Altman plots and Deming regression for agreement between measurement methods.
- meta_analysis: Conducts fixed/random effects meta-analysis with forest plots and heterogeneity tests.
Use Cases
- A clinical researcher evaluates a new biomarker by calling roc_analysis to determine sensitivity/specificity thresholds from patient data.
- An epidemiologist uses survival_analysis to compare treatment outcomes across cohorts in a study pipeline.
- A lab developer integrates method_comparison into a validation script for new assay instruments.
- A meta-analyst runs meta_analysis on published trial data to synthesize effect sizes.
Who This Is For
Biostatisticians analyzing clinical trials, medical researchers performing diagnostics validation, healthcare data scientists building analysis tools, and developers creating health informatics applications.