Survival Analysis
Survival analysis in R, including Kaplan-Meier, Cox models, competing risks, RMST, and multi-state models.
Install
$ agentstack add skill-choxos-biostatagent-survival-analysis ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-choxos-biostatagent-survival-analysis) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Survival Analysis Patterns
Overview
Comprehensive survival analysis methods in R covering Kaplan-Meier estimation, Cox proportional hazards models, parametric survival models, and advanced techniques for time-to-event data.
Basic Survival Objects
Creating Survival Data
library(survival)
# Right-censored data (most common)
surv_obj
forestplot(
mean = hr,
lower = hr_lower,
upper = hr_upper,
labeltext = c(variable, n, hr_text),
is.summary = is_summary
)Parametric Survival Models
With survreg
# Weibull model
weibull_fit
ggcuminc()
# Fine-Gray model (tidy interface)
crr(Surv(time, status) ~ treatment + age, data = df, failcode = 1)Restricted Mean Survival Time
library(survRM2)
# RMST comparison
rmst_result
set_engine("survival") |>
set_mode("censored regression")
# Workflow
surv_wf
add_formula(Surv(time, status) ~ treatment + age + sex) |>
add_model(cox_spec)
# Fit
surv_fit <- fit(surv_wf, data = train_data)
# Predict survival probability
predict(surv_fit, new_data, type = "survival", eval_time = c(12, 24, 36))
# Predict hazard
predict(surv_fit, new_data, type = "hazard", eval_time = c(12, 24, 36))Key Packages Summary
| Package | Purpose | |---------|---------| | survival | Core survival functions | | survminer | KM plots and Cox visualization | | flexsurv | Parametric models | | rstpm2 | Flexible parametric (Royston-Parmar) | | cmprsk | Competing risks | | tidycmprsk | Tidy competing risks | | survRM2 | RMST analysis | | mstate | Multi-state models | | censored | tidymodels integration |
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: choxos
- Source: choxos/BiostatAgent
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.