bio-clinical-biostatistics-multiplicity-graphical
Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.
npx skills add BioTender-max/awesome-bio-agent-skills --skill multiplicity-graphical --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
Guides construction of multiplicity control strategies for confirmatory clinical trials with multiple endpoints, covering graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle. Addresses FDA guidance on multiple endpoints and distinguishes FWER vs FDR. Targets primary + key secondary + subgroup hierarchies and uses graphical procedures via the R package gMCP to allocate alpha and propagate it through a predefined graph. Includes examples and decision guidance for various scenarios and endpoint configurations.
How it works
Describes a closed-testing framework where a hypothesis is rejected only if all intersection hypotheses are rejected by valid local tests. Presents an algorithmic taxonomy of procedures (Bonferroni, Holm, Hochberg, Hommel, Fixed-sequence, Parallel/Serial/Mixed gatekeeping, Graphical procedures, and their Simes/parametric extensions) and how they control FWER under different dependence assumptions. Provides practical R code snippets using gMCP to construct a graph, set initial weights, define transitions for alpha propagation, and run the graphical procedure with p-values at a given alpha. Also documents standard graph patterns (hierarchical, Holm, primary+secondaries, co-primary chains, subgroup branches) and how to implement them in gMCP.
When to use it
Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints, or when hierarchical or gatekeeping structures are desired to control the trial-wide FWER while accommodating complex endpoint hierarchies.
What it can touch
R environment with gMCP package; p-values from the trial; optional correlation handling via Simes or parametric extensions; example code references graphMCP(graph, pvalues, alpha, ...), and standard SAP graph patterns.
Caveats
References versions and compatibility notes for R packages (gMCP 0.8.16+; graphicalMCP 0.2+; gatekeeping; multcomp; multXpert) and advises verifying installed versions. Discusses theoretical aspects of closed testing and admissibility; notes entangled vs non-entangled graphs and coherence considerations in memory-bearing graphs. No guaranteed outcomes are stated; emphasizes design choices and regulatory contexts per FDA guidance.
## Version Compatibility Reference examples tested with: R `gMCP` 0.8.16+, `graphicalMCP` 0.2+, `gatekeeping`, `multcomp`, `multxpert`; Python `statsmodels` 0.14+ for basic FDR/FWER methods. Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` - Python: `pip show <package>` then `help(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. # Multiplicity Control for Confirmatory Trials **"Design the multiplicity strategy for my trial"** -> Specify a closed-testing procedure (graphical, gatekeeping, hierarchical, or step-down Bonferroni-Holm) that controls family-wise error rate at the trial-wide level across primary endpoints, key secondary endpoints, and subgroup analyses, with provable strong FWER control. ## The Foundational Theorem -- Closed Testing Is Necessary **Marcus, Peritz & Gabriel 1976 *Biometrika* 63:655:** a hypothesis H_I (I ⊆ {1,...,m}) is rejected iff every intersection hypothesis ∩_{J⊇I} H_J is rejected by a valid α-level local test. Strong FWER control holds for AN
- Version Compatibility
- The Foundational Theorem -- Closed Testing Is Necessary
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Bretz-Maurer Graphical Procedures -- The Modern Standard
- gMCP R package
- Standard SAP graph patterns
- Bretz et al 2011 -- Simes and parametric extensions
- Maurer-Bretz 2013 entangled graphs
- Gatekeeping Procedures
- Serial gatekeeping (hierarchical)
- Parallel gatekeeping (Dmitrienko 2003)
- Mixed / multistage (Dmitrienko-Tamhane 2008)
- Hochberg vs Hommel vs Holm
What does the bio-clinical-biostatistics-multiplicity-graphical skill do?
Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill multiplicity-graphical --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.