graphicalmcp

name	graphicalMCP
description	Guide users through graphical multiple comparison procedures using the graphicalMCP R package. Use this skill when the user asks about: multiplicity graphs, Bonferroni-based procedures, graph_create, graph_test_shortcut, graph_update, transition matrices, alpha reallocation, or closed testing with graphs.

Graphical Multiple Comparison Procedures with graphicalMCP

Note: This skill targets graphicalMCP >= 0.2.9 (CRAN, github.com/openpharma/graphicalMCP). Version 0.2.9 fixed precision issues in parametric tests (#90).

API reference

• Full function docs: references/llms.txt (source: https://openpharma.github.io/graphicalMCP/)
• Workflow patterns: references/code_patterns.md

Key functions

Graph creation and manipulation

• graph_create() - Create a multiplicity graph (hypotheses, weights, transitions)
• graph_update() - Update graph by deleting rejected hypotheses
• as_graph() - Convert from gMCP, igraph, or matrix objects

Testing

• graph_test_shortcut() - Shortcut (Bonferroni-based) graphical testing
• graph_test_closure() - Full closure-based testing (supports Simes, parametric)
• graph_rejection_orderings() - Enumerate all valid rejection orderings

Adjusted p-values and weights

• adjust_p() - Adjusted p-values (Bonferroni, Simes, parametric)
• adjust_weights() - Adjusted significance levels (Bonferroni, Simes, parametric)
• graph_generate_weights() - Generate weights for all intersection hypotheses

Power

• graph_calculate_power() - Power simulation via multivariate normal

Plotting

• plot.initial_graph() - Plot multiplicity graph
• plot.updated_graph() - Plot updated graph sequence

Example graphs

• example_graphs() - Pre-built example graphs (Bonferroni-Holm, fixed sequence, etc.)

Workflow patterns

For detailed code templates, read references/code_patterns.md.

Topics covered:

• Creating graphs with graph_create() (weights, transitions)
• Common graph structures (fixed sequence, Bonferroni-Holm, fallback, successive)
• Shortcut (Bonferroni) testing with graph_test_shortcut()
• Closure testing with graph_test_closure() (Simes, parametric, Hochberg, mixed)
• Updating graphs manually with graph_update()
• Generating intersection weights with graph_generate_weights()
• Rejection orderings with graph_rejection_orderings()
• Power simulation with graph_calculate_power() (marginal power, correlation)
• Custom success criteria for power (e.g., "reject primary + secondary")
• Plotting graphs (layout, edge curves, epsilon edges, colors)
• Built-in example graphs (20+ pre-built procedures)
• Converting between graphicalMCP, gMCPLite, and igraph formats
• Parametric tests with known correlation (nested populations, shared controls)
• Multi-population multi-endpoint designs (oncology PFS + OS in subgroup + overall)

Important design considerations

• graph_test_shortcut() vs graph_test_closure(): Shortcut is Bonferroni-only and faster; closure supports Simes, parametric, and Hochberg tests for more power
• Simes test: Valid when test statistics are positively correlated (PRDS condition); more powerful than Bonferroni for correlated hypotheses
• Parametric test: Requires known test_corr between test statistics; most powerful when correlation is high
• test_groups and test_types: Allow different test types for different groups of hypotheses (e.g., parametric for co-primary, Simes for secondary)
• test_corr: Must match test_types in length; use NA for non-parametric tests (Bonferroni, Simes, Hochberg)
• sim_corr vs test_corr: sim_corr is for generating correlated p-values in power simulation; test_corr is the known correlation used in the parametric test itself
• power_marginal: Marginal power for each hypothesis at full alpha (not at allocated alpha); higher values = stronger signal
• Transition matrix rows must sum to ≤ 1: Each row represents how alpha propagates when that hypothesis is rejected
• Sequential p-values with multiplicity: Sequential p-values can be passed directly to graph_test_shortcut() to test multiple hypotheses in group sequential trials while controlling FWER.
  • For gsDesign objects (from gsSurv(), gsSurvCalendar(), gsSurvPower()): use gsDesign::sequentialPValue()
  • For gsDesign2 objects (from gs_design_ahr()): use gsDesign2::sequential_pval()
  • See Maurer & Bretz (2013) and the graphicalMCP-gsDesign2 skill.