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 missing-data-sensitivitygit 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/missing-data-sensitivity)<a href="https://agentmods.dev/skills/gptomics/bioskills/missing-data-sensitivity"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/missing-data-sensitivity/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/missing-data-sensitivity"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/missing-data-sensitivity.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 | $0.00120 | $0.08172 |
| Opus 5 | $0.00060 | $0.04086 |
| Sonnet 5 | $0.00024 | $0.01634 |
| Haiku 4.5 | $0.00012 | $0.00817 |
Grade A, and why
bio-clinical-biostatistics-missing-data 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-missing-data — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: R mmrm 0.3+ (Roche/openpharma), R rbmi 1.5+ (Roche/Bayer via insightsengineering), R mice 3.16+, R mitools 2.4+, Python sklearn 1.4+, statsmodels 0.14+.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_name - Python:
pip show <package>thenhelp(module.function)
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Missing Data Sensitivity for Confirmatory Trials
"Handle missing data in a confirmatory clinical trial" -> Pre-specify the missing-data assumption per ICH E9(R1); execute the primary analysis under the chosen assumption (typically MAR via MMRM or MI); run clinically-articulable MNAR sensitivity analyses (reference-based MI per Carpenter-Roger 2013); report the tipping delta that would overturn the conclusion (Permutt 2016).
The Foundation -- NRC 2010 and ICH E9(R1)
The U.S. National Research Council Panel ("The Prevention and Treatment of Missing Data in Clinical Trials," 2010; chaired by Roderick Little; Little, D'Agostino, Cohen et al 2012 NEJM 367:1355): 18 recommendations grouped as prevention (Recs 1-7), analysis (Recs 8-14), sensitivity (Recs 15-18).
Key recommendations:
- Rec 10: explicitly REJECT LOCF and BOCF as default; they are biased even under MCAR
- Rec 13: endorses WGEE (weighted GEE) for marginal estimands
- Rec 15: "examining sensitivity to assumptions about the missing-data mechanism should be a mandatory component of reporting"
ICH E9(R1) (2019) forces the ordering: define the estimand (5 attributes including ICE strategy) BEFORE choosing the analysis. The missing-data strategy maps to the ICE handling strategy:
- Treatment policy ICE strategy + missing post-ICE data -> reference-based MI (J2R typical)
- Hypothetical ICE strategy -> MMRM under MAR; g-computation
- Composite ICE strategy -> ICE becomes part of endpoint; no missing-data problem for that subject
- While-on-treatment ICE strategy -> pre-ICE values only; censored at ICE
- Principal stratum -> latent stratum, requires Bayesian or sensitivity over unverifiable assumptions
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 · 437 lines · 120 tokens per session scan A 5f438483ea24
bio-clinical-biostatistics-missing-data is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 120 tokens to every session and 8,172 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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