gsdesign2

name: gsDesign2 description: > 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.

Group Sequential Design with gsDesign2

Note: This skill targets gsDesign2 >= 1.1.8 (main branch at github.com/Merck/gsDesign2). The llms.txt from gsDesign.ai may lag behind; local API docs are in llms_local.txt.

API reference

• Full function docs (CRAN): references/llms.txt (source: https://gsDesign.ai)
• Full function docs (local v1.1.8+): references/llms_local.txt
• Workflow patterns: references/code_patterns.md

Key functions

Design (solve for sample size)

• gs_design_ahr() - AHR-based group sequential design (primary workhorse)
• gs_design_wlr() - Weighted logrank design
• gs_design_rd() - Rate difference design (now with minimum risk weighting)
• gs_design_combo() - Combination test design
• gs_design_npe() - Non-proportional effect design (general)
• fixed_design() - Fixed (non-sequential) designs

Power (given fixed assumptions)

• gs_power_ahr() - Power for AHR designs
• gs_power_wlr() - Power for weighted logrank designs
• gs_power_rd() - Power for rate difference designs
• gs_power_combo() - Power for combination tests
• gs_power_npe() - Power for general NPE designs

Information and bounds

• gs_info_ahr() / gs_info_wlr() / gs_info_rd() / gs_info_combo() - Statistical information
• gs_spending_bound() - Spending function bounds
• gs_spending_combo() - Spending bounds for combination tests
• gs_b() - Fixed boundary values
• gs_bound_summary() - Formatted bound summary (supports multiple alpha levels)

Analysis

• gs_update_ahr() - Update bounds with observed data (supports stratified piecewise events)
• gs_cp_npe() - Conditional power under NPH
• sequential_pval() - Sequential p-value for AHR designs (new in 1.1.9)

Enrollment and failure rates

• define_enroll_rate() - Piecewise enrollment rates (supports strata)
• define_fail_rate() - Piecewise failure rates with HR (supports strata)

Utilities

• ahr() / ahr_blinded() - Average hazard ratio computation
• expected_accrual() / expected_event() / expected_time() - Expected quantities
• pw_info() - Piecewise information
• to_integer() - Integer sample size rounding
• ppwe() / s2pwe() - Piecewise exponential utilities
• wlr_weight() - Weight functions for weighted logrank

Output

• as_gt() / as_rtf() - Table output (footnotes can be suppressed)
• summary() / text_summary() - Text summaries
• gs_bound_summary() - Bound summary table

Workflow patterns

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

Topics covered:

• Enrollment and failure rate setup (piecewise, stratified, scaling to target N)
• Fixed designs and group sequential designs with gs_design_ahr()
• Power computation with gs_power_ahr() (event = NULL caveat)
• Non-proportional hazards scenarios (delayed effect, crossing, diminishing)
• Spending functions and bound specification
• Spending time (decoupling spending from information fraction)
• Rate difference designs for binary endpoints
• Weighted logrank and MaxCombo combination tests
• Integer rounding with to_integer()
• Sequential p-values with sequential_pval() for multiplicity
• Updating bounds with gs_update_ahr() (stratified piecewise events)
• Conditional power with gs_cp_npe()
• Output and reporting (gt, RTF, bound summary)
• info_scale options (h0_info, h1_info, h0_h1_info)
• Stratified designs (subgroup + complement populations)

Important design considerations

• info_frac = NULL: Use with analysis_time to let timing drive the design; gsDesign2 derives the information fraction
• event = NULL in gs_power_ahr: Always set this when using analysis_time; the default c(30, 40, 50) causes length mismatches
• info_scale = "h0_info": Matches gsDesign convention; recommended for multiplicity workflows
• Spending time: Use timing in upar/lpar to decouple spending from information fraction (critical for delayed effects and multiple hypotheses)
• Stratified designs: Use stratum column in define_enroll_rate() and define_fail_rate(); scale complement enrollment from subgroup using prevalence
• sequential_pval() (v1.1.9+): Works directly with gs_design_ahr() output; replaces the need to call gsDesign::sequentialPValue() with manual conversion
• gs_update_ahr() event_tbl: Supports piecewise event tables for delayed-effect designs where events per interval are tracked
• Non-binding futility: Use binding = FALSE so efficacy bounds are computed ignoring the futility bound
• Spending functions as strings: sf = "sfLDOF" works in addition to sf = sfLDOF