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.
npx skills add GPTomics/bioSkills --skill survival-analysisgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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.
[](https://agentmods.dev/skills/gptomics/bioskills/survival-analysis)<a href="https://agentmods.dev/skills/gptomics/bioskills/survival-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/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.
<a href="https://agentmods.dev/skills/gptomics/bioskills/survival-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/survival-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00116 | $0.09509 |
| Opus 5 | $0.00058 | $0.04755 |
| Sonnet 5 | $0.00023 | $0.01902 |
| Haiku 4.5 | $0.00012 | $0.00951 |
Grade A, and why
bio-clinical-biostatistics-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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-clinical-biostatistics-survival-analysis — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 469 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: lifelines 0.27+, scikit-survival 0.21+, statsmodels 0.14+, pandas 2.1+, numpy 1.26+. R packages cited (still the SOTA for survival): survival 3.8+, survRM2, cmprsk, riskRegression, mstate, flexsurv, icenReg, rpsftm.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_name
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Time-to-Event Analysis for Clinical Trials
"Analyze time-to-event endpoint" -> Estimate a hazard, survival probability, cumulative incidence, or restricted mean time using a method calibrated to (a) whether proportional hazards holds, (b) whether competing events exist, (c) whether censoring is informative, and (d) which estimand the trial targets under ICH E9(R1).
The Single Most Important Modern Insight -- PH Almost Never Holds
In modern oncology with checkpoint inhibitors, targeted therapies, crossover, and depleted high-risk subjects over follow-up, proportional hazards (PH) violations are the rule, not the exception. The Cross-Pharma NPH Working Group (Lin et al 2020 Stat Biopharm Res; Magirr-Burman 2021 Stat Biopharm Res 15(2):295) documented systematic PH violations across phase III oncology trials, particularly delayed-effect patterns from checkpoint inhibitors.
The Cox HR is a time-averaged log-hazard ratio under PH violation (Xu-O'Quigley 2000), which may or may not be the estimand of interest. RMST (Royston-Parmar 2013) provides a clinically interpretable, hazard-free alternative.
Algorithmic Taxonomy
| Method | Estimand | Inference | Strength | Fails when |
|---|---|---|---|---|
| Log-rank (unstratified) | Test of S_A(t) = S_B(t) all t | Permutation / asymptotic chi-square | Standard; preserves Type-I under PH | Underpowered under non-PH; treats all events equally |
| Stratified log-rank | Same null within strata, pooled | Asymptotic | Preserves stratification factor from randomisation | Stratification factor must be pre-specified |
| Weighted log-rank G(rho, gamma) | Direction-specific test under non-PH | Asymptotic | High power for delayed/early/middle effects | Weight choice must match true effect time profile; chasing weight = p-hacking |
| Cox PH | Conditional log-HR | Wald, LR, score | Standard; semi-parametric; covariate adjustment | PH violation makes HR a misleading summary; check via cox.zph |
| Stratified Cox | Same; baseline hazards differ by stratum | Wald | Handles non-PH by stratification | Loses inference on stratification variable; cannot interact treatment with strata |
Time-varying Cox (tt()) |
Time-dependent log-HR | Wald | Quantifies non-PH explicitly | Interpretability — no single "the HR"; choose g(t) carefully |
| Flexible parametric (Royston-Parmar) | Time-varying log-HR via splines | Wald | Smooth S(t), HR(t); supports extrapolation | Spline choice affects results; software in R stpm2/stpm3 |
| RMST | Difference in mean survival truncated at tau | Wald with delta or pseudo-obs regression | Hazard-free; clinically interpretable in time units | tau choice; min follow-up across arms constrains tau |
| MaxCombo | Maximum over weighted log-rank family | Asymptotic multivariate normal | Robust to range of NPH patterns | Can reject in opposite directions on same data (Magirr 2022 critique) |
| Fine-Gray subdistribution HR | Conditional subdistribution HR | Wald | Direct CIF modeling | Andersen-Keiding 2012 critique: violates causal hazard semantics |
| Cause-specific Cox | Conditional cause-specific HR | Wald | Causally interpretable | Predicts hazards, not CIFs; need both for CIF prediction |
| Multi-state Cox (mstate) | Transition-specific HRs | Wald | Subsumes competing risks; handles relapse/remission | More complex; more parameters to estimate |
| Andersen-Gill (recurrent) | Rate ratio | Robust (cluster) Wald | Most efficient under exchangeability | Assumes exchangeable events |
| PWP (recurrent) | Conditional event-order HR | Stratified Wald | Handles event-order qualitative heterogeneity | More strata = more parameters; smaller per-stratum n |
| Interval-censored Cox (NPMLE) | Cumulative hazard | Likelihood ratio | Correct for periodic-assessment data | Slower; software in R icenReg |
What ships with it
2 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.
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.
- 6d ago First seen · 469 lines · 116 tokens per session scan A 0c7e52fe9d06
bio-clinical-biostatistics-survival-analysis is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 116 tokens to every session and 9,509 once invoked, about $0.0006 per session on Opus 5. 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 2026-09-03.
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