What is survival-analysis?

A guide for analysing how long it takes for events such as cancer outcomes to occur, including cases where a study ends before the event happens. It covers common statistical methods such as Kaplan-Meier curves, Cox models, competing-risk analysis, and restricted mean survival time.

Use it to analyse clinical time-to-event data, compare survival groups, test risk factors, check model assumptions, study competing risks, or choose biomarker cutoffs.

It provides a structured way to compare patient outcomes over time while accounting for incomplete follow-up and other possible events.

survival-analysis — Skill for Claude Code and Codex by zamushwani

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add zamushwani/biomedical-ai-skills --skill survival-analysis

Clone the repo

git clone --depth 1 https://github.com/zamushwani/biomedical-ai-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/survival-analysis/github.svg)](https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/survival-analysis)

Your own site

<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/survival-analysis"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/survival-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/survival-analysis"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/survival-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,698 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.04698
Opus 5.5	$0.00000	$0.01879
Sonnet 5.5	$0.00000	$0.00940
Haiku 4.5	$0.00000	$0.00470

Measured 2mo ago against content hash f4deb87031fc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

survival-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/survival-analysis/SKILL.md · 452 lines

How it starts

The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

library(TCGAbiolinks)
library(SummarizedExperiment)

query <- GDCquery(
  project = "TCGA-GBM",
  data.category = "Transcriptome Profiling",
  data.type = "Gene Expression Quantification",
  workflow.type = "STAR - Counts"
)
GDCdownload(query, directory = "GDCdata")
se <- GDCprepare(query, directory = "GDCdata")
clinical <- as.data.frame(colData(se))

# Construct survival endpoint from TCGA clinical fields
# TCGA stores time in days. Convert to months for readability.
clinical$os_time <- ifelse(!is.na(clinical$days_to_death),
  as.numeric(clinical$days_to_death),
  as.numeric(clinical$days_to_last_follow_up))
clinical$os_event <- ifelse(clinical$vital_status == "Dead", 1, 0)
clinical$os_months <- clinical$os_time / 30.44

# Drop samples with missing survival data
clinical <- clinical[!is.na(clinical$os_months) & clinical$os_months > 0, ]

Read the full file on GitHub · 452 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 452 lines · 0 tokens per session scan A f4deb87031fc

Subscribe to this mod's changes

survival-analysis is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,698 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metrics DESeq2 Scientific ai-agent ai-skills bioinformatics cancer-genomics cancer-research

survival-analysis next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
survival-analysiszamushwani/biomedical-ai-skills	Skill	1	1mo ago	MIT	A	0	Claude Code, Codex
automated-soap-note-generatoraipoch/medical-research-skills	Skill	2.0k	1mo ago	MIT	A	51	Claude Code, Codex
estimate-immune-score-analysisaipoch/medical-research-skills	Skill	2.0k	1mo ago	MIT	A	90	Claude Code, Codex
external-model-validationaipoch/medical-research-skills	Skill	2.0k	1mo ago	MIT	A	66	Claude Code, Codex
gsva-analysis-and-visualizationaipoch/medical-research-skills	Skill	2.0k	1mo ago	MIT	A	88	Claude Code, Codex
hierarchical-clustering-plotaipoch/medical-research-skills	Skill	2.0k	1mo ago	MIT	A	74	Claude Code, Codex
immune-pathway-analysisaipoch/medical-research-skills	Skill	2.0k	1mo ago	MIT	A	50	Claude Code, Codex