# Survival Analysis > A Claude skill from zamushwani2/biomedical-ai-skills. - **Type:** Skill - **Install:** `agentstack add skill-zamushwani2-biomedical-ai-skills-survival-analysis` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [zamushwani2](https://agentstack.voostack.com/s/zamushwani2) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [zamushwani2](https://github.com/zamushwani2) - **Source:** https://github.com/zamushwani2/biomedical-ai-skills/tree/main/skills/survival-analysis ## Install ```sh agentstack add skill-zamushwani2-biomedical-ai-skills-survival-analysis ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Survival Analysis Time-to-event analysis for cancer clinical data. Covers Kaplan-Meier, Cox proportional hazards, competing risks, restricted mean survival time, and optimal cutpoint selection using the survival, ggsurvfit, tidycmprsk, and survRM2 packages. ## When to Use This Skill Activate when the user requests: - Kaplan-Meier survival curves with risk tables - Log-rank or stratified log-rank tests between groups - Cox proportional hazards modeling (univariable or multivariable) - Proportional hazards assumption checking - Competing risks analysis (cause-specific or Fine-Gray) - Restricted mean survival time (RMST) comparisons - Optimal biomarker cutpoint selection for survival - Forest plots for multivariate Cox models ## Inputs | Data Type | Format | Source | |-----------|--------|--------| | Clinical | Tabular (time, event, covariates) | TCGA GDC via TCGAbiolinks, cBioPortal | | Time variable | Days/months to event or last follow-up | `days_to_death`, `days_to_last_follow_up` | | Event indicator | Binary (0 = censored, 1 = event) | Derived from `vital_status` | | Covariates | Categorical or continuous | Age, stage, gene expression, mutations | --- ## Preparing TCGA Survival Data ```r library(TCGAbiolinks) library(SummarizedExperiment) query 0, ] ``` ## Kaplan-Meier Estimation ```r library(survival) # v3.8+ library(ggsurvfit) # v1.2+, pharmaverse — modern replacement for survminer # survfit2() tracks the calling environment for cleaner legend labels # Use survfit2() with ggsurvfit; use survfit() with base R plotting km_fit log-rank is most powerful Curves cross or separate late (common in immunotherapy) -> log-rank has low power. Use RMST or weighted log-rank. > 2 groups -> log-rank gives a global test. Follow up with pairwise comparisons using p.adjust() for multiple testing correction. ``` ## Cox Proportional Hazards ### Univariable ```r # Continuous covariate cox_age = 10 events per predictor in the model. With 50 events, fit at most 5 covariates. More recent work (Riley 2019) recommends >= 20 EPV for adequate precision. Too many covariates relative to events = overfitting and unstable HRs. Continuous vs categorical: Keep continuous variables continuous in Cox models. Dichotomizing age at median discards information and reduces power. If a cutpoint is needed for clinical communication, derive it separately (see Optimal Cutpoints below) and validate externally. ``` ## Proportional Hazards Assumption ```r # Schoenfeld residual test zph 0.05: PH assumption not rejected (but doesn't prove PH holds) # p PH holds # Systematic trend (slope) -> PH violated, effect changes over time plot(zph) # Or with survminer: survminer::ggcoxzph(zph) ``` ``` Interpreting Schoenfeld residuals: Positive slope: effect increases over time (HR drifts upward) Negative slope: effect weakens over time (early benefit that fades) U-shape or crossing zero: complex time-varying effect The p-value is sample-size dependent: large datasets reject PH for clinically negligible departures. Always look at the plot. ``` ### Handling PH Violations ```r # Option 1: Time-varying coefficient with tt() # Preferred when a specific covariate violates PH cox_tv HR is more efficient. Report both HR and RMST. Curves cross or separate late (immunotherapy, delayed effect)? -> RMST captures the difference that HR misses. Need a clinically interpretable number? -> RMST: "Treatment gives 3.2 extra months of survival on average" HR: "Treatment reduces the hazard by 30%" (less intuitive) ``` ## Optimal Cutpoint Selection ```r library(survminer) # v0.5+, provides surv_cutpoint # maxstat approach: maximally selected log-rank statistic # Finds the cutpoint that maximally separates high/low groups cp 14%, PSA > 4 ng/mL) - Communication to non-statistical audiences Use continuous modeling for primary analysis, cutpoints for clinical translation. ``` ## Forest Plots ```r library(forestmodel) # v0.6+ # Directly from a coxph object — one function call forest_model(cox_multi) # Produces a ggplot2-based forest plot with HRs, 95% CIs, p-values # Customize forest_model(cox_multi, factor_separate_line = TRUE, # categorical levels on separate lines format_options = forest_model_format_options( text_size = 3.5, point_size = 3 ) ) # Alternative: survminer::ggforest() for quick plots # Limitation: does not handle interactions or spline terms properly survminer::ggforest(cox_multi, data = clinical) ``` ## Output Specification | Output | Format | Description | |--------|--------|-------------| | `km_curves.pdf` | PDF | Kaplan-Meier curves with risk table and p-value | | `cox_summary.csv` | CSV | HR, 95% CI, p-value for each covariate | | `cox_zph.pdf` | PDF | Schoenfeld residual plots for PH assumption | | `forest_plot.pdf` | PDF | Multivariate Cox forest plot | | `cif_curves.pdf` | PDF | Cumulative incidence curves for competing risks | | `rmst_results.csv` | CSV | RMST difference, ratio, CI, p-value | | `cutpoint_analysis.csv` | CSV | Optimal cutpoint, corrected p-value, HR at cutpoint | ## Validation Checks ``` After running survival analysis on TCGA-GBM, verify: Overall survival: Median OS for all GBM: ~14-16 months If median OS > 24 months: likely includes IDH-mutant cases (not true GBM under WHO 2021) 2-year survival: ~20-25% IDH1 mutation (strongest single prognostic factor in glioma): IDH-mutant median OS: ~31 months IDH-wildtype median OS: ~14-16 months Log-rank p-value: should be 80) Proportional hazards: Age typically satisfies PH in GBM (chronic risk factor) Treatment effect may violate PH (delayed separation with TMZ) If global cox.zph p = 10 rule (preferably >= 20). 5. **Stepwise selection for final models**: Forward/backward stepwise selection inflates Type I error and produces non-reproducible models. Pre-specify covariates based on clinical knowledge. 6. **Ignoring collinearity**: Stage and tumor size are correlated. Including both in a Cox model inflates standard errors and produces unstable HRs. Check VIF or pairwise correlations before fitting. ### Competing Risks 7. **Standard KM for competing risks endpoints**: KM treats competing events as censored, which inflates cumulative incidence of the event of interest. Use cumulative incidence functions (CIF) via `tidycmprsk::cuminc()` when competing events are present. 8. **Interpreting Fine-Gray as causal**: Fine-Gray SDH measures the effect on cumulative incidence, not on the rate among those at risk. A covariate that increases competing event risk will artificially appear to decrease the subdistribution hazard for the primary event. ### RMST and Cutpoints 9. **Post-hoc tau selection**: Choosing tau after seeing the data invalidates the RMST test. Pre-specify tau based on clinical context (standard follow-up duration, planned analysis timepoint). 10. **Cutpoint without validation**: An optimal cutpoint found by maxstat will appear highly significant in the discovery cohort even for a non-prognostic biomarker. Without independent validation, the cutpoint is a hypothesis, not a result. ## Related Skills - [`cancer-multiomics`](../cancer-multiomics/SKILL.md): TCGA data retrieval, mutation status and gene expression for Cox covariates - [`immune-deconvolution`](../immune-deconvolution/SKILL.md): Immune cell fraction estimates as continuous survival predictors ## Public Datasets for Testing | Dataset | Samples | Use Case | |---------|---------|----------| | TCGA-GBM | 611 | Glioblastoma, strong IDH1/MGMT prognostic markers, poor prognosis | | TCGA-BRCA | 1098 | Breast cancer, molecular subtype-specific survival differences | | TCGA-LUAD | 585 | Lung adenocarcinoma, EGFR/KRAS mutation-driven survival | | TCGA-OV | 608 | Ovarian cancer, classic for competing risks (progression vs death) | ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [zamushwani2](https://github.com/zamushwani2) - **Source:** [zamushwani2/biomedical-ai-skills](https://github.com/zamushwani2/biomedical-ai-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-zamushwani2-biomedical-ai-skills-survival-analysis - Seller: https://agentstack.voostack.com/s/zamushwani2 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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