
Guide users through graphical MCP procedures using the gMCPLite R package (legacy). Use this skill when the user asks about: hGraph for multiplicity graph visualization, gMCP for closed testing, or legacy graphical MCP workflows. For new projects, prefer graphicalMCP.
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.
Guide users through classical group sequential trial design using the gsDesign R package. Use this skill when the user asks about: group sequential boundaries, spending functions (sfLDOF, sfHSD, sfPoints), sample size for time-to-event or binomial trials, gsDesign objects, plotting group sequential bounds, gsSurvPower for power computation, or harm bounds (test.type 7/8).
Guide users through sample size calculation, group sequential design, and simulation for clinical trials with negative binomial (recurrent event) outcomes using the gsDesignNB R package. Use this skill when the user asks about: negative binomial sample size, recurrent event trials, overdispersed counts, event gaps, rate ratios, Wald test for count data, seasonal event rates, blinded or unblinded sample size re-estimation, group sequential designs for negative binomial endpoints, or the Zhu-Lakkis method.
Guide users through multi-endpoint group sequential trial simulation with multiplicity-controlled testing. Use this skill when the user asks about: simulating trials with OS, PFS, and ORR endpoints, illness-death model simulation with gsDesign bounds, sequential p-values in simulation loops, combining graphicalMCP with gsDesign for simulation-based operating characteristics, cumulative rejection probabilities, or building a full pipeline from design through simulation to multiplicity-adjusted testing.
Guide users through confirmatory adaptive clinical trial design and analysis using the rpact R package. Use this skill when the user asks about: adaptive designs, sample size reassessment, conditional power, inverse normal combination test, Fisher combination test, multi-stage designs, or rpact design objects.
Guide users through clinical trial simulation using the simtrial R package. Use this skill when the user asks about: simulating survival trials, simfix, sim_pw_surv, cutting data at calendar or event times, weighted logrank tests, MaxCombo tests, or simulation-based power.
Guide users through group sequential design with graphical multiplicity control using the graphicalMCP and gsDesign2 R packages. Use this skill whenever the user asks about: group sequential designs with multiple hypotheses, graphical multiplicity testing, sequential p-values with gsDesign2, combining graphicalMCP with gsDesign2, clinical trial designs with multiple endpoints and populations, Maurer-Bretz procedures, alpha-spending with multiplicity graphs, or adapting the gMCPLite vignette template. Also trigger when users mention spending time, information fraction, or sequential p-values in the context of group sequential or graphical testing.
Guide users through next-generation group sequential design using the gsDesign2 R package. Use this skill when the user asks about: gs_design_ahr, gs_power_ahr, gs_update_ahr, sequential_pval, average hazard ratio designs, non-proportional hazards, piecewise enrollment/failure rates, spending time, or information fraction computation.
Guide users through weighted parametric group sequential design using the wpgsd R package. Use this skill when the user asks about: correlated test statistics across hypotheses, generate_bounds, closed_test, correlation matrices for nested populations, or parametric multiplicity adjustment with group sequential designs.
Guide users through simulating clinical trials using the illness-death model with response. Use this skill when the user asks about: multi-state models for oncology trials, simulating correlated OS/PFS/ORR endpoints, transition rates between disease states, illness-death model calibration, building ADTTE datasets from simulation, analysis cut date determination, or theoretical survival curves from transition rates.