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 choxos/BiostatAgent --skill group-sequential-methodsgit clone --depth 1 https://github.com/choxos/BiostatAgentWrote 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/choxos/biostatagent/group-sequential-methods)<a href="https://agentmods.dev/skills/choxos/biostatagent/group-sequential-methods"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/group-sequential-methods/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/choxos/biostatagent/group-sequential-methods"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/group-sequential-methods.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 | $0.00034 | $0.02444 |
| Opus 5.5 | $0.00014 | $0.00978 |
| Sonnet 5.5 | $0.00007 | $0.00489 |
| Haiku 4.5 | $0.00003 | $0.00244 |
Grade A, and why
group-sequential-methods 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.
How it starts
The opening of the file, as written. The whole thing — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Group Sequential Methods
When to Use This Skill
- Designing group sequential trials with interim analyses
- Implementing alpha spending functions
- Setting futility stopping rules
- Calculating information fractions
- Using sim_gs_n() for GS simulations
- Integrating with gsDesign2 package
Fundamental Concepts
Group Sequential Design
A group sequential design allows for:
- Early stopping for efficacy: If treatment effect is larger than expected
- Early stopping for futility: If treatment effect is unlikely to reach significance
- Reduced expected sample size: When treatment effect is present
Information Fraction
Information fraction at analysis k:
I_k / I_K = (events at analysis k) / (total planned events)
For time-to-event trials, information ≈ number of events.
Type I Error Spending
The key constraint is that the design controls the overall Type I error at the planned alpha level. Spending functions define cumulative alpha spending over information time, and boundaries are derived using the joint distribution of sequential test statistics. They are not obtained by simply assigning independent nominal alpha levels to each look.
Alpha Spending Functions
O'Brien-Fleming (OBF)
Properties:
- Conservative at early analyses
- Nearly full alpha at final analysis
- Difficult to stop early
- Maintains nominal Type I error
Formula:
α*(t) = 2 - 2Φ(z_{α/2} / √t)
When to Use:
- Want maximum power at final analysis
- Early efficacy stopping unlikely
- Regulatory preference for conservative early bounds
Pocock
Properties:
- Equal spending at each analysis
- Easier to stop early
- Inflated final alpha
- Lower power at final analysis
Formula:
α*(t) = α × log(1 + (e-1)t)
When to Use:
- Early stopping is a priority
- Treatment effect expected to be large
- Willing to sacrifice final analysis power
Hwang-Shih-DeCani (HSD)
Properties:
- Flexible family indexed by γ
- γ = -4: Similar to OBF
- γ = 1: Similar to Pocock
- γ = 0: Linear (Pocock-like)
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 · 347 lines · 34 tokens per session scan A 20c699a4513a
group-sequential-methods is a skill published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 2,444 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
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