bio-clinical-biostatistics-trial-reporting
Prepares statistical reports for clinical trials following CONSORT 2025, SPIRIT 2025, ICH E9(R1) estimands, and FDA 2023 covariate adjustment guidance. Covers Table 1 generation, analysis populations (ITT/FAS/PP/Safety), the 5 ICH E9(R1) intercurrent-event strategies, MMRM under MAR (mmrm), reference-based MI (rbmi J2R/CR/CIR), Permutt tipping-point sensitivity, and Rubin's-rules vs frequentist variance debate. Use when preparing regulatory submissions, defining estimands, or implementing missing-data sensitivity analyses.
npx skills add BioTender-max/awesome-bio-agent-skills --skill trial-reporting --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Prepares statistical reports for clinical trials following CONSORT 2025, SPIRIT 2025, ICH E9(R1) estimands, and FDA 2023 covariate guidance. It covers Table 1 generation, analysis populations (ITT/FAS/PP/Safety), the five ICH E9(R1) intercurrent-event strategies, MMRM under MAR (mmrm), reference-based MI (rbmi J2R/CR/CIR), Permutt tipping-point sensitivity analyses, and Rubin's-rules vs frequentist variance debate. It is intended to be used when preparing regulatory submissions, defining estimands, or implementing missing-data sensitivity analyses.
How it works
- Specifies estimands before selecting statistical methods and uses covariate-adjusted primary analyses aligned with the estimand.
- Supports five intercurrent-event strategies (Treatment policy, Hypothetical, Composite, While-on-treatment, Principal stratum) and prescribes corresponding analytical approaches (e.g., MMRM under MAR for hypothetical, retrieved-dropout MI for treatment policy).
- Recommends MMRM as FDA-preferred for continuous longitudinal endpoints under MAR, with details on the model terms, covariance structures, and KR/Satterthwaite options.
- Provides code patterns for mmrm in R (mmrm package) and rbmi workflows (rbmi package) for reference-based imputation (J2R/CR/CIR) and Rubin's rules vs alternative variance estimators.
- Includes guidance on sensitivity analyses (Permutt tipping-point) and the regulatory context (Aducanumab, Wegovy, Aprocitentan cases).
When to use it
- When preparing regulatory submissions and defining estimands under ICH E9(R1).
- When planning missing-data strategies and regulatory-grade sensitivity analyses.
- When generating Table 1 and selecting appropriate analysis populations for trial reports.
What it can touch
- Tools: primary_tool: tableone
- R tools and code blocks for MMRM ('mmrm' package) and rbmi workflows, including sample R code for model fitting and imputation/pooling.
Caveats
- References and examples rely on specific package versions (tableone 0.9+, mmrm, rbmi) and regulatory interpretations; verify installed versions match the cited signatures.
- The material discusses methodological debates (Rubin's rules vs frequentist variance) and regulatory acceptability, which may vary by agency and case; ensure SAP explicitly documents the chosen approach.
- License: NOASSERTION
## Version Compatibility Reference examples tested with: tableone 0.9+, statsmodels 0.14+, scikit-learn 1.4+, pandas 2.1+, numpy 1.26+. R packages cited (essential for current regulatory work): mmrm 0.3+ (Roche/openpharma), rbmi 1.5+ (Roche/Bayer via insightsengineering), gMCP, RBesT. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(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. # Trial Reporting Under CONSORT 2025 + ICH E9(R1) **"Prepare a clinical trial statistical report"** -> Define the estimand explicitly per ICH E9(R1); execute a covariate-adjusted primary analysis targeting the right summary measure; pre-specify the missing-data strategy and run regulatory-grade sensitivity analyses; structure the output per CONSORT 2025 and the new SPIRIT 2025 alignment. ## The Single Most Important Methodological Shift -- The Estimand Comes First **Kahan, Cro, Li, Harhay 2023 *Am J Epidemiol* 192:987 ("Eliminating Amb
- Version Compatibility
- The Single Most Important Methodological Shift -- The Estimand Comes First
- The Five Intercurrent-Event Strategies
- Decision Tree for Estimand Selection
- MMRM -- The FDA-Favoured MAR Analysis
- The mmrm R package (Roche / openpharma)
- Convergence-vs-correctness trade-off
- MMRM = hypothetical estimator (Olarte Parra unification)
- Reference-Based Multiple Imputation -- The rbmi Framework
- The Variance Debate -- Cro/Carpenter vs Bartlett/Wolbers
- Permutt Tipping-Point Analysis -- The Analyst as Adversary
- Decisive Regulatory Cases -- The 2020-2025 Casebook
- Table 1 -- Baseline Characteristics
- Analysis Populations -- ITT vs FAS vs PP vs Safety
What does the bio-clinical-biostatistics-trial-reporting skill do?
Prepares statistical reports for clinical trials following CONSORT 2025, SPIRIT 2025, ICH E9(R1) estimands, and FDA 2023 covariate adjustment guidance. Covers Table 1 generation, analysis populations (ITT/FAS/PP/Safety), the 5 ICH E9(R1) intercurrent-event strategies, MMRM under MAR (mmrm), reference-based MI (rbmi J2R/CR/CIR), Permutt tipping-point sensitivity, and Rubin's-rules vs frequentist variance debate. Use when preparing regulatory submissions, defining estimands, or implementing missing-data sensitivity analyses.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill trial-reporting --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.