group-sequential-design

name	group-sequential-design
description	Design group sequential clinical trials for survival endpoints (OS, PFS, DFS) with interim analyses, spending functions, boundaries, multiplicity, and event/enrollment prediction. Triggers on: Phase 3 trial design, sample size/events for survival endpoints, alpha spending, group sequential design, interim analysis planning, or enrollment/event timeline prediction for clinical trials.

Bundled Resources

File	What it contains	When to read
reference.md	Design guidance, parameter tables, spending functions, key rules, failure modes, analysis framework, protocol references	After collecting user inputs, before writing R code
examples.md	All R code examples organized by design pattern	When you need code for a specific pattern — read only the relevant section
post_design.md	IA timing checks (warning messages, user options), verification procedure (pass criteria, lrsim code, log template)	After computing the design (step 6), before delivering
scripts/gsd_report_template.py	Bundled Python report template — reads gsd_results.json, generates gsd_report.docx with zero hardcoded values	Step 9: copy to output dir and run (or extend for design-specific features)

Do NOT read these files upfront. Read them only when you reach the corresponding step.

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Compact Instructions

When the conversation gets long and context is compressed, preserve information in this priority order:

1. Architecture decisions — never re-summarize these; they are settled
2. Key changes — what was computed, what parameters were chosen
3. Current verification status — pass/fail only, drop verbose logs
4. Tool outputs — can be deleted; keep only essential numbers (events, boundaries, power, sample size)

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Workflow

Task Progress Tracking: At the start of step 6 (after user confirms inputs), create a task checklist using TaskCreate for all remaining steps. Mark each task in_progress when starting and completed when done. Typical tasks:

1. Write and run R design script (gsd_design.R)
2. Check IA timing constraints
3. Run verification simulation (gsd_verification.R)
4. Generate Word report (gsd_report.py)

This gives the user a visual progress indicator (spinner → checkmark) throughout the computation.

Pacing — one artifact per turn. Do not bundle multiple major artifacts into a single response. Each of these is its own turn:

• Writing gsd_design.R (then stop — let the tool result come back)
• Running gsd_design.R (then stop)
• Writing gsd_verification.R
• Running the verification
• Writing the report

When you call Write for a script, do not also include long reasoning or a parallel Bash execution in the same turn. Save explanations for the final report. This keeps each assistant turn well under the output token cap and avoids truncation.

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0. Scope check — do this before anything else.

   This skill supports standard group sequential designs (GSD) for survival endpoints (OS, PFS, DFS). Before collecting any inputs, assess whether the user's request falls within this scope.

   In scope — proceed to Step 1:

   • Fixed set of hypotheses tested at pre-planned interim and final analyses
   • Survival endpoints (time-to-event): OS, PFS, DFS, EFS
   • Single or co-primary endpoints; single or multiple nested populations (subgroup + ITT)
   • Pre-specified alpha spending, futility boundaries, multiplicity graphs
   • Non-proportional hazard evaluation as a sensitivity analysis on a fixed GSD

   Out of scope — stop and explain. If the user's request matches any of the patterns below, do NOT proceed with the GSD workflow. Instead, reply with the message template at the end of this step.

   Pattern	Key signals in the user's description
   Adaptive enrichment	"enrich to biomarker-positive patients based on interim data", "narrow the population if the subgroup is responding", "population selection at interim", "pick the responders mid-trial"
   Sample size re-estimation (SSR)	"increase the sample size based on interim results", "blinded or unblinded SSR", "adaptive sample size", "re-estimate events or N at interim"
   Dose selection / seamless Phase 2/3	"select the best dose at interim and continue", "drop losing arms", "pick the winner dose", "seamless adaptive Phase 2/3"
   Platform / master protocol	"add or remove treatment arms dynamically", "master protocol", "basket or umbrella trial arm management"
   Non-survival primary endpoint	Binary response rate, continuous endpoint, count data as the primary — note: survival co-primaries alongside a binary endpoint are borderline; ask the user to clarify which endpoint drives the GSD
   Adaptive randomization	"response-adaptive randomization", "update allocation ratios based on interim data"

   How to assess: Read the user's message and look for the signals above. A design with a biomarker subgroup AND an ITT population tested at fixed analyses is in scope (multi-population GSD). A design where the subgroup definition or the set of hypotheses changes based on interim data is out of scope (adaptive enrichment). When ambiguous, ask one clarifying question: "Will the set of populations or hypotheses being tested change based on unblinded interim data?" — Yes → out of scope; No → in scope.

   Out-of-scope reply template:

   This request is outside the scope of the group-sequential-design skill.

   The group-sequential-design skill supports standard GSD for survival endpoints with a fixed set of hypotheses, pre-planned analyses, and pre-specified alpha spending. It does not support designs where the trial adapts its structure (populations, arms, sample size, or hypotheses) based on unblinded interim data.

   Your request appears to involve [name the specific design type, e.g., "adaptive enrichment" / "sample size re-estimation" / "seamless dose-selection"], which requires a different statistical framework — typically an inverse-normal combination test, closed testing with combination functions, or a platform trial methodology (tools: rpact, ADDPLAN, or simulation-based approaches).

   If I've misread your design and it does use a fixed set of hypotheses and pre-planned analyses, please clarify and I'll proceed. Otherwise, I'd suggest consulting a statistician specializing in adaptive designs or referring to FDA/EMA adaptive design guidance documents.

1. Create output subfolder — If the user specifies an output directory path (e.g., "write all outputs to /some/path"), use that exact path as out_dir and create it. Otherwise, immediately after the user answers Q1 (disease/setting), create output/gsd_{disease}_{endpoints}_{YYYYMMDD}/ (e.g., output/gsd_1l_mnsclc_pfs_os_20260327/). Use a placeholder for {endpoints} if not yet known (e.g., output/gsd_1l_mnsclc_20260327/), and rename later once endpoints are confirmed. ALL outputs — including any exploratory plots or comparisons generated during the Q&A phase — go in this subfolder.

2. Collect inputs — Ask the questions below, one at a time

3. Summarize and confirm — Present a clean table, get user confirmation. If the prompt states all inputs are confirmed or is an automated run, skip this step and proceed immediately.

4. Read reference.md — Review design guidance, key rules, and failure modes

5. Read relevant sections of examples.md — Get code for the chosen design pattern

6. Write and run the R design script — Compute boundaries, save results to gsd_results.json, generate multiplicity_diagram.png via graphicalMCP::graph_create() + plot(). Save all outputs to the subfolder created in step 1. gsd_results.json must include all user-specified constraints (min_followup, min_gap, max_N, feasible_range, power_target, n_sim) so the report script can reference them without hardcoding.

   Transition matrix validation (step-down designs only). Immediately after defining the transition matrix in the R script, call validate_transition_matrix(tm, gate_prereqs) (see examples.md → "Transition Matrix Validation"). This function checks Rule 3 programmatically and stops with an error if any transition routes alpha to a hypothesis that must already be rejected. The script must not proceed to boundary computation if validation fails.

   Piecewise control hazards: When the control arm has a piecewise hazard (e.g., different median survival in early vs late periods), use gsSurv(lambdaC=c(log(2)/m1, log(2)/m2), S=breakpoint, ...) — do NOT write custom event calculation functions. gsSurv() natively supports piecewise hazards via the lambdaC vector and S breakpoint parameters. This ensures correct event accrual, timing, and boundary computation. Similarly, use nSurv() with the same piecewise parameters for baseline calculations.

   N-first approach. N is a top-level design parameter — all results (IA timing, FA timing, events, power) depend on N. The design script follows three phases:

   • Phase A: Determine starting N — Compute required events via Schoenfeld, estimate minimum N from events and prevalence, pick starting N from user's feasibility range (Q13b). Derive R from enrollment ramp. See reference.md → "N-First Design Algorithm".
   • Phase B: Design at fixed N — All calculations use fixed R/N. Find IA time (event-driven), derive OS IF, compute boundaries. No gsSurv() for enrollment sizing.
   • Phase C: Evaluate and adjust — If requirements aren't met (power, timing, OS IF), present N adjustment as an option alongside other levers. Re-run Phase B.

   IA/FA timing must be event-driven. The design script should:

   1. Compute the required events for each hypothesis that has a power target
   2. Find the calendar time when those events accrue (event-driven timing)
   3. Set IA time = max(event-driven times for IA-tested hypotheses, enrollment end + min follow-up)
   4. Set FA time = max(event-driven time for OS power, IA + min gap)
   5. Report which constraint is binding (event target vs min follow-up vs min gap)

   For single-look endpoints with derived power (not targeted), do NOT include their event requirement in the IA timing calculation — their power is whatever results from the events available at the IA.

   Multiplicity diagram: The node labels must show the actual initial alpha (e.g., 0.005), not the fraction of total alpha. Pass the raw alpha values directly as hypotheses — graphicalMCP displays whatever values you pass. Use hyp_names for the hypothesis name + endpoint (e.g., "H1\nPFS-Ext") and precision = 4 in plot().

6b. Check for over-powered hypotheses — After computing boundaries and power for all hypotheses, check if any hypothesis has power more than 3 percentage points above the target (e.g., >93% when target is 90%). If so, present a table showing each hypothesis's power and flag the over-powered ones. Suggest alpha reallocation: reduce the over-powered hypothesis's alpha and shift it to the hypothesis with the least margin above the power target. This is especially important for:

• Step-down designs where gated hypotheses (tested at full alpha 0.025) are inherently over-powered
• Co-primary designs where one endpoint has a stronger HR than the other
• Multi-population designs where the ITT population has many more events than the subgroup

Direction of reallocation matters: Always reallocate alpha FROM the overpowered endpoint TO the underpowered one. For example, if PFS has power 97% and OS has power 90%, suggest reducing PFS alpha (e.g., 0.005 → 0.003) and increasing OS alpha (e.g., 0.020 → 0.022). Never suggest increasing alpha for an already-overpowered endpoint — that makes the imbalance worse.

Alpha reallocation comparison table: When suggesting alpha reallocation, always include a comparison table showing the current design vs the proposed reallocation, at the same fixed N. The table should show:

Metric	Current (PFS 0.005 / OS 0.020)	Proposed (PFS 0.003 / OS 0.022)
PFS power	97.5%	94.1%
OS power	90.0%	91.2%
PFS Z boundary	2.576	2.748
OS Z boundary (FA)	2.109	2.082
Events (unchanged)	...	...
IA / FA timing (unchanged)	...	...

This lets the user see the concrete impact of the reallocation — how much power shifts between endpoints — before deciding. Compute the alternative boundaries in the R script and store both options in gsd_results.json (e.g., "alpha_reallocation_comparison" array).

See reference.md → "Handling Over-Powered Hypotheses" for the full options (increase alpha vs accept derived power).

7. Check IA timing vs enrollment — Compare each interim analysis calendar time against the enrollment duration. If ANY interim analysis occurs before enrollment completes, warn the user and present options to fix it (see "IA Timing vs Enrollment Check" section below). Do NOT proceed until the user confirms the design or adjusts it. 7b. NPH Evaluation (only if user specified NPH in Q7b) — With the PH design complete (events, boundaries, enrollment from steps 1–7), evaluate it under the NPH assumptions. See "NPH Evaluation Workflow" section below. Present the NPH evaluation table to the user. If power under NPH is unacceptable, discuss options before proceeding:

   • Add looks for NPH robustness: If there is a large gap between analyses AND the endpoint's AHR improves over time, suggest adding the endpoint to an additional analysis. This gives the endpoint a second chance at a later timepoint with better AHR. See reference.md → "Adding Looks for NPH Robustness" for the full strategy. When evaluating timing options for the additional analysis, always compute and compare the NPH endpoint's power at each option — the timing should be driven by NPH power, not just the triggering endpoint's IF.
   • Alpha reallocation: Shift alpha from an over-powered endpoint to the NPH-affected endpoint
   • Increase N: More events improve AHR over time (modest effect)
   • Reduce N to push analyses later (counterintuitive but effective for delayed-effect NPH): With fewer patients, events accrue more slowly, so analyses happen at later calendar times when the AHR has improved (closer to the late-period HR). This is especially powerful when: (a) the delayed effect period is short (e.g., 3 months) relative to the analysis timeline, and (b) the late-period HR is substantially better than the overall PH HR. Trade-off: the study enrolls fewer patients but runs longer. Present an N sensitivity table showing how reducing N affects: analysis calendar times, AHR at each analysis, NPH power, and total study duration. Often a modest N reduction (e.g., 700 → 620) can push the FA 6–12 months later, improving AHR from 0.80 to 0.76 and gaining 5–8 pp of NPH power.
   • Relax min follow-up or min gap constraints: If these constraints are binding (forcing analyses earlier than event-driven timing would dictate), relaxing them allows the event-driven schedule to determine timing — which naturally produces later analyses with better AHR under NPH.
   • Accept lower NPH power: If NPH is a sensitivity analysis, not the primary design basis

8. Verify via simulation — Run lrstat simulation. Save verification script and log in the subfolder. If verification FAILS, investigate and fix the design before proceeding. (If NPH was specified, run verification under BOTH PH and NPH assumptions — the NPH verification must include both power under H1-NPH AND type I error under H0-NPH. See post_design.md → "NPH verification" for the required log format.)

   Store verification results in gsd_results.json. After verification passes, write all simulation outputs (simulated power, type I error, simulated events, simulated timing per analysis) into gsd_results.json under a "verification" key. The report script must read these from JSON — never hardcode simulation result values (e.g., "90.4%", "2.40%", "598") directly in gsd_report.py. This is the #2 source of hardcoding after sensitivity tables.

9. Generate Word report via Python — Only after verification passes. Use the bundled report template at scripts/gsd_report_template.py as the starting point. This template is structurally hardcoding-free — it reads all values from gsd_results.json and generates the 6-section report dynamically.

   How to use the template:

   1. Copy scripts/gsd_report_template.py to the output subfolder as gsd_report.py
   2. Review whether the JSON structure from gsd_design.R matches what the template expects (field names like total_N, os_z_upper, os_hr_upper, etc.)
   3. If the JSON uses different field names, either (a) adjust the R script to use the expected names, or (b) add a small mapping section at the top of the copied script — but do NOT hardcode any design values
   4. If the design has features not covered by the template (e.g., NPH evaluation, 2-IA alternative, multi-population), extend the template by adding new sections that read from JSON — following the same pattern of j("key") lookups
   5. Run: python gsd_report.py <output_dir> — always execute the report script and verify gsd_report.docx was created. Do not consider this step complete until the .docx file exists. If the script errors, fix it and rerun. This is non-negotiable — in past iterations, the report script was written but never executed, resulting in a missing .docx. Always run the script and confirm the file exists before proceeding.

   Why a template? Writing the report from scratch each time is the #1 source of hardcoded values across all evaluations. The template eliminates this structurally — every number flows from JSON by construction. Extending the template for a specific design is much less error-prone than writing 400+ lines from scratch.

   If you must write from scratch (e.g., the design structure is too different from what the template handles), follow these rules strictly:

   • Every number must come from gsd_results.json via r["key"] or equivalent
   • Narrative text like "meeting the 6-month gap requirement" must use r["min_gap"]
   • Threshold strings like "≥ 90%" must be built: f">= {r['power_target']}%"
   • Sensitivity tables, verification results, and comparison tables must be stored in gsd_results.json as arrays/objects first, then looped over in the report script: for row in data["sensitivity_table"]: ...
   • After writing, grep for numeric literals and fix any that match design values
   • Self-check: if you find yourself typing a number (e.g., "K=13", "90.4%", "450 patients"), stop — put it in gsd_results.json first and read it from there

10. Copy all scripts — Copy gsd_design.R, gsd_report.py, and gsd_verification.R into the output subfolder.

11. Deliver outputs — Report the subfolder path, summarize results, and present the key strengths/limitations to the user

Design Iteration

• Comparison table: When the user requests a modification, automatically produce a before/after table showing all changed metrics. Bold improved values. Present before proceeding with verification.
• Ask before leaping: When a structural change (merging IAs, changing triggers) makes parameters free or constrained differently, ask the user before re-deriving. See reference.md → "Design Iteration Guardrails".
• Over-powered hypotheses: See reference.md → "Handling Over-Powered Hypotheses" for options (increase alpha vs accept derived power).

Sample Size and Study Duration

• N ↔ duration trade-off: Fewer patients → slower event accrual → longer study. Always present both N and timing in the comparison table. See reference.md → "High OS IF at IA" for the full mechanism.
• OS IF > 85%: Increasing N can lower OS IF at IA. Run an N sensitivity table. See reference.md → "High OS IF at IA".
• Short-survival diseases (median OS 8–10 mo): The N-duration effect is disproportionately large. If FA is too late, present N sensitivity table first — modest N increases often solve the problem. See reference.md → "Short-survival amplification".
• N sensitivity table values must be consistent with the enrollment ramp. Iterate over the last enrollment period duration K and compute actual N = sum(gamma × R). Do NOT use arbitrary round numbers.
• N sensitivity table must include operational columns — not just N/events/power. Required columns: K (months) | N | Events_IA | Events_FA | Enroll(mo) | IA(mo) | FA(mo) | Study(mo) | MinFU(mo) | IA-FA Gap(mo) | Power(%). This lets users compare the full operational picture — a design with slightly more patients but 12 fewer months of study duration is often preferable.
• Store the sensitivity table in gsd_results.json as a "sensitivity_table" array of objects so the report can loop over it without hardcoding any values.

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AskUserQuestion

Collect design parameters by asking ONE question at a time. Wait for the user's response before moving to the next question. Do NOT provide defaults or recommendations. Always present options and let the user choose.

AskUserQuestion Format

• Write for a smart 16-year-old. Plain English, no jargon, no R function names, no implementation details.
• Use concrete examples and analogies. Instead of "information fraction", say "how far along the study is when you peek at the data."
• Always offer lettered options. A) ... B) ... C) ... — never mark one as recommended or default.
• Keep it short. One question, one paragraph max of context.
• No defaults, no recommendations. Present all options neutrally and wait for the user to decide.

Questions (ask in this order)

1. "What disease are you studying, and what line of therapy? (e.g., first-line metastatic NSCLC, second-line bladder cancer)"

2. "How many patient populations will be tested in this trial?"

   • A) One population — everyone is analyzed together (e.g., all-comers, or a single biomarker-selected group)
   • B) Two nested populations — a biomarker-positive subgroup AND the overall/ITT population (e.g., PD-L1 CPS ≥ 20 subgroup + all patients)
   • C) Three nested populations — two biomarker subgroups AND the overall population (e.g., CPS ≥ 20, CPS ≥ 1, all patients)
   • D) Other — describe

   (If B, C, or D — multi-population):

   2b. "What is the biomarker and what defines each subgroup? (e.g., 'PD-L1 TPS ≥ 50% subgroup' and 'all patients (ITT)')"

   2c. "What fraction of the total enrolled patients do you expect to fall in each subgroup? The broadest population is always 100%."

   • Example for 2 populations: Biomarker+ subgroup: ___% (e.g., 50%), Overall/ITT: 100%

3. "What's the main thing you're measuring — how long patients live overall (OS), how long before the tumor grows (PFS), or both as co-primary endpoints?"

   • A) Overall survival (OS)
   • B) Progression-free survival (PFS)
   • C) Disease-free survival (DFS)
   • D) Co-primary — two endpoints tested together (e.g., PFS + OS)
   • E) Other — specify your endpoint

   (If multi-population) "Are the same endpoints tested in every population?"

   • A) Yes — same endpoints in all populations (e.g., PFS + OS in both subgroup and ITT)
   • B) No — different endpoints by population (specify)

   After Q3, enumerate all hypotheses for the user. Example for 2 populations with PFS+OS: "Your design has 4 hypotheses: H1 (PFS in subgroup), H2 (PFS in ITT), H3 (OS in subgroup), H4 (OS in ITT)."

4. (Only if 2+ hypotheses — co-primary, multi-population, or both) Multiplicity strategy. This is asked early because it defines the testing framework for everything else.

   4a. "What is the overall one-sided significance level (alpha) for the study? In most Phase 3 trials, this is 0.025 (one-sided), which is equivalent to 0.05 two-sided."

   • A) 0.025 (one-sided) — standard
   • B) Other — specify

   4b. (If multi-population) "How should the populations be tested?" See reference.md → "Choosing Between Step-Down and Alpha Split" for guidance.

   • A) Step-down — test the subpopulation first at full alpha. Only if positive, test the overall population. The subpopulation drives the sample size; the overall population is typically overpowered. Best when there is strong belief that the subpopulation has a better treatment effect.
   • B) Alpha split — divide the alpha between populations upfront. Each population is tested independently. Best when uncertain whether the subpopulation is truly superior.

   (If B — alpha split) When allocating alpha, assign more alpha to the subpopulation than to the overall population — the subpopulation has fewer events (due to prevalence) and needs the alpha advantage. The overall population has more events and can maintain power with less alpha. See reference.md → "Alpha Allocation in Alpha-Split Designs" for the full rationale and caveats.

   4c. "How do you want to distribute the initial alpha across your hypotheses? The amounts must sum to the total alpha."

   (Single-population co-primary) Example: PFS 0.005 + OS 0.020 = 0.025.

   (Multi-population step-down) All initial alpha goes to the subpopulation. Example: H1 (PFS-sub) = 0.005, H3 (OS-sub) = 0.020, H2 (PFS-ITT) = 0, H4 (OS-ITT) = 0.

   (Multi-population alpha-split) More alpha to subpopulation (fewer events, needs the advantage). Example: H1 (PFS-sub) = 0.003, H2 (PFS-ITT) = 0.002, H3 (OS-sub) = 0.015, H4 (OS-ITT) = 0.005.

   Validation: If the split does not sum to the total alpha, flag the discrepancy and ask the user to correct it.

   4d. (If multi-population AND step-down only) "What is the testing order within each chain?"

   Skip this question for alpha-split designs. In an alpha-split, every hypothesis receives initial alpha and is testable from the start — there is no gating. Proceed directly to Q4e.

   For step-down: subpopulation hypothesis must be rejected before the corresponding overall hypothesis can be tested. Additionally, for co-primary endpoints where OS > PFS: OS must be positive in a population before PFS in a broader population can be tested.

   Present the gating structure for confirmation. Example (step-down, PFS+OS):

   • H3 (OS-sub) must be rejected before H4 (OS-ITT) can be tested
   • H4 (OS-ITT) must be rejected before H2 (PFS-ITT) can be tested (OS positive = population positive)
   • H1 (PFS-sub) is tested initially with its allocated alpha (no gate)

   4e. "When a hypothesis is rejected, where should its freed alpha flow?"

   Before applying any recycling rules, trace the gating chain for EVERY hypothesis. For each hypothesis H, list which other hypotheses must already be rejected for H to be testable. These are H's "gate prerequisites" — H can NEVER send alpha to any of them (they are already rejected). Write out this table explicitly before constructing the transition matrix:

   Hypothesis	Gate prerequisites (already rejected when H is tested)	Eligible recipients
   H1	(none — tested initially)	H2, H3, H4
   H2	(none — tested initially)	H1, H3, H4
   H3	H2, H4 (gated behind both)	H1 only
   H4	H2 (gated behind H2)	H1, H3

   Only after completing this table, apply the Alpha Recycling Priority Rules to the eligible recipients only:

   • Rule 1: PFS rejection → OS same population
   • Rule 2: OS rejection → OS next population
   • Rule 3: Do not pass alpha to already-rejected hypotheses (gating constraint) — enforced by the table above
   • Rule 4: Split alpha when multiple hypotheses could benefit
   • Rule 5: Use 1.0 vs 0.999 based on intent:
     • 1.0 when only one hypothesis is intended to receive alpha (e.g., PFS → OS same population — unambiguous, single destination)
     • 0.999/0.001 when multiple hypotheses could benefit (e.g., OS rejection — primary target gets 0.999, alternate gets 0.001 epsilon)
     • Always confirm with the user when proposing weight = 1.0 — even when the rationale seems clear, present the reasoning and let the user confirm or adjust

   Epsilon destination is a strategic choice. When an OS hypothesis is rejected and epsilon goes to a PFS hypothesis, there are two valid strategies — present both and let the user decide:

   • Epsilon to PFS in the smaller population (fewer events, benefits more from alpha boost)
   • Epsilon to PFS in the same population as the rejected OS (to lock in both co-primary endpoints in that population first)

   Present the derived transition matrix and ask: "Does this alpha flow look right?"

   (Single-population co-primary) Simpler version: "If one endpoint wins, should its alpha flow to the other?"

   • A) Full bidirectional reallocation
   • B) One-way (PFS → OS only)
   • C) One-way (OS → PFS only)
   • D) No reallocation

5. "How are patients split between the new treatment and the standard?"

   • A) 1:1 — one patient on the new drug for every one on the standard
   • B) 2:1 — two patients on the new drug for every one on the standard
   • C) 3:1 — three patients on the new drug for every one on the standard
   • D) Other — specify a ratio

6. "On the standard treatment, how long do patients typically survive (or stay progression-free) before half of them have an event? This is the 'median' in months."

   Piecewise control hazards: If the user specifies a piecewise control hazard (e.g., "log(2)/4 for the first 3 months, then log(2)/8 thereafter"), accept it. This is common when early event rates differ from later rates (e.g., high early progression risk that decreases). Store as lambdaC = c(log(2)/4, log(2)/8) with breakpoint S = 3. Use calc_expected_events_pw() from examples.md for event calculations and gsSurv(lambdaC=..., S=...) for boundary computation. For required events, use Schoenfeld formula (not nSurv) when the HR is constant — see reference.md → "Schoenfeld vs nSurv with piecewise control hazards".

   (If multi-population) Ask per population × endpoint. Offer shortcut: "If medians are the same across populations, just give one number per endpoint."

   • Control median PFS in subgroup? (e.g., 8 months)
   • Control median PFS in ITT? (e.g., 6 months)
   • Control median OS in subgroup? (e.g., 18 months)
   • Control median OS in ITT? (e.g., 14 months)

7. "How much better do you expect the new treatment to be? This is expressed as a hazard ratio — lower means bigger benefit."

   • A) 0.80 — modest benefit (~20% risk reduction)
   • B) 0.75 — moderate benefit (~25% risk reduction)
   • C) 0.70 — strong benefit (~30% risk reduction)
   • D) 0.65 — large benefit (~35% risk reduction)
   • E) Other — specify a number

   (If multi-population) Ask per population × endpoint. "The treatment effect is often stronger in the biomarker-enriched subgroup."

7b–7c. Non-proportional hazards (NPH) — do NOT ask proactively. Only collect NPH specifications if the user explicitly requests an NPH evaluation. If they do, collect piecewise specs per endpoint (control hazard and HR by time period). The Q6/Q7 answers become the PH reference design; NPH specs are used only for evaluation afterward. See "NPH Evaluation Workflow" section for details.

8. (Only if co-primary endpoints in Q3) "For your co-primary endpoints, do the interim analyses happen at the same time for both endpoints, or does each endpoint have its own separate schedule?"

   • A) Same time — both endpoints are analyzed at every interim look and at the final analysis
   • B) Separate schedules — each endpoint has its own number of interims and its own timing

   If A: proceed to Q8a. If B: proceed to Q8b.

8a. (Same-time IAs) "How many total analyses (interims + final)?"

• A) 1 interim + final (2 total looks)
• B) 2 interims + final (3 total looks)
• C) 3 interims + final (4 total looks)
• D) No interim — just one final analysis
• E) Other

Then: "Which endpoint triggers each analysis?"

• Give examples based on number chosen.

Immediately after establishing triggers, ask: "Which endpoints are tested at which analysis?" An endpoint that triggers an analysis is always tested there, but a non-triggering endpoint may or may not be tested at every analysis. For example, if PFS triggers the IA and OS triggers the FA, PFS might be tested only at the IA (single look) or at both IA and FA.

Present a table for confirmation showing which endpoint × analysis combinations are active. Example:

Analysis	PFS	OS
IA (PFS-triggered)	Tested	Tested
FA (OS-triggered)	?	Tested

"Is PFS also tested at the FA, or only at the IA?" This determines whether PFS is a single-look (k=1) or multi-look endpoint, which affects boundary computation.

(If multi-population) Expand the table to show all hypotheses (H1–H4) instead of just endpoints.

8b. (Separate schedules) Ask for each endpoint individually.

(Single endpoint — skip Q8/8a/8b) "How many times do you want to peek at the data before the final analysis?" (same options as 8a)

9. (Skip if no interims) "When should the interim peek(s) happen? This is how far along the study should be — measured by what fraction of the total expected events have occurred."

   • Single-look triggering endpoint (k=1): If the endpoint that triggers an analysis is only tested once at that analysis (e.g., PFS tested only at the IA), there is NO information fraction to ask — the IA timing is fully determined by the number of events needed for that endpoint's test (driven by power, alpha, and HR). The non-triggering endpoint's IF at that analysis is calculated from the shared timeline. Skip Q9 entirely for this case.
   • Multi-look triggering endpoint: Only ask the information fraction for the triggering endpoint at each analysis. The non-triggering endpoint's IF is calculated, not asked.
   • If co-primary separate schedules: ask fractions separately per endpoint
   • Common choices: 50%, 60%, 75% (1 IA); 33%/67%, 50%/75% (2 IAs)
   • Key concept: Information fraction only applies to endpoints with multiple looks. For a single-look endpoint, 100% of its events are used at its only analysis — there is no fraction to choose.

   (If multi-population) After computing the design, present the derived event table for confirmation:

   Analysis	PFS events (sub)	PFS events (ITT)	OS events (sub)	OS events (ITT)
   IA1	~NNN	~NNN	~NNN	~NNN
   FA	—	—	~NNN	~NNN

   "Do these event counts look reasonable, or would you like to adjust?"

10. (Skip if no interims) "How do you want to spend your alpha (false-positive budget) across the interim and final analyses?"

    • A) Conservative early, save most for the final look — Lan-DeMets O'Brien-Fleming (sfLDOF)
    • B) Moderately conservative — Hwang-Shih-DeCani gamma=-4 (sfHSD, gamma=-4)
    • C) Moderate — Hwang-Shih-DeCani gamma=-2 (sfHSD, gamma=-2)
    • D) Aggressive early, spread evenly — Lan-DeMets Pocock (sfLDPocock)
    • E) Other

    (If multi-population) "Should the same spending function be used for all hypotheses?"

    • A) Yes — same for all
    • B) No — specify per endpoint or per population

11. "How sure do you need to be that the trial will detect a real benefit if it exists? Higher power means a bigger study."

    • A) 80% — standard, smaller study
    • B) 85% — moderate confidence
    • C) 90% — higher confidence, larger study
    • D) Other — specify a percentage

    (If co-primary endpoints) Whether to ask power for each endpoint depends on what triggers its last look:

    • If an endpoint's last look is triggered by itself → its power is a free parameter — ask the user.
    • If an endpoint's last look is triggered by another endpoint → its power is derived — calculate it, don't ask.

    Example 1: IA1→PFS, IA2→OS, FA→OS. Only ask OS power. Example 2: IA→PFS, FA→OS. Ask power for both.

    (If multi-population) Ask power for the lead hypothesis that drives the study size (typically OS in the subpopulation for step-down, or OS in the broadest population for alpha-split). Gated hypotheses have their power calculated.

12. (Skip if no interims) "Should the study also be able to stop early for futility — i.e., if the drug clearly isn't working?"

    • A) Yes, with a non-binding rule (advisory, not forced)
    • B) Yes, with a binding rule (must stop if crossed)
    • C) No — efficacy stopping only

    (If co-primary or multi-population) Also ask which hypothesis/hypotheses should have futility stopping.

    (If futility requested) "How should the futility boundary be determined?"

    • A) Beta spending — boundary controls probability of stopping under H1.
    • B) Under the null — boundary set for high probability of stopping when HR=1.

    Event inflation — "non-binding" is asymmetric in gsDesign:

    • Binding (test.type=3): Beta spending inflates events (both alpha and power assume binding).
    • Non-binding (test.type=4): Also inflates events, but less than binding. "Non-binding" only applies to alpha (efficacy boundaries ignore futility). Power is still computed as if futility IS binding — trials that cross the futility bound under H1 are treated as lost, so more events are needed to maintain target power. The inflation is modest (~0.5% for gamma=-20, ~3% for gamma=-4, ~5% for gamma=-2). See reference.md → "Beta spending futility and sample size" for the full explanation.
    • Under the null: No event inflation.

    (If beta spending) "Which spending function for futility?"

    • A) HSD gamma=-2 — moderate (aggressive boundary)
    • B) HSD gamma=-4 — conservative
    • C) HSD gamma=-6 — very conservative
    • D) HSD gamma=-20 — minimal (boundary rarely crossed under H1)
    • E) Other

13. "How fast do you expect to enroll patients?"

    • A simple steady rate (e.g., "15 patients/month")
    • A ramp-up schedule (e.g., "5/month for 3 months, then 10/month, then 15/month")

13b. "What range of total sample size do you consider feasible for this trial? This helps us check whether the computed design falls within practical limits." - Example: "600–900 patients" or "no more than 800" - If the user provides a range, store it. After the design is computed, compare the resulting total N against this range. If N falls outside the range, flag it and discuss options (adjust alpha split, relax power target, change enrollment, etc.). - Infeasibility check: If the minimum N required to meet the power target exceeds the user's upper bound, this is an infeasible constraint. Do NOT silently exceed the limit — explicitly flag it: "The minimum sample size for these design parameters is N_min = XXX, which exceeds your constraint of < YYY. Options: (a) relax the N constraint, (b) reduce power target, (c) increase HR assumption, (d) adjust alpha allocation." Present trade-offs and let the user decide before proceeding. - N sensitivity exploration must center around the user's stated range. The user's range reflects what is feasible and desired — anchor exploration there. If the user says "less than 600", explore e.g., 500–600 in steps of 20. If the user says "600–900", explore that range. Do NOT start from the computed minimum N and work up — that wastes the user's time on infeasible or uninteresting values. - N values must be consistent with the enrollment ramp. Do NOT use arbitrary round numbers (e.g., 520, 560, 600). Instead, compute the actual N that results from varying the last enrollment period duration. With a ramp of 5/mo×2mo + 20/mo×3mo + 30/mo×Kmo, the only free parameter is K (months of steady-state enrollment). Iterate over K values and report the resulting N = 5×2 + 20×3 + 30×K = 70 + 30K. For example: K=15→520, K=16→550, K=17→580, K=18→610. This ensures every N in the sensitivity table is achievable with the stated enrollment rates. - N sensitivity must include a "what-if" row up to 5% above the constraint. When the design is near the N constraint boundary, always include at least one row in the sensitivity table that exceeds the constraint by up to 5% (e.g., if the constraint is N < 450, include the next achievable N up to ~472). Label it clearly as "exceeds constraint" and show the improvement in study duration, IA-FA gap, or power. This gives the user concrete data on the cost of relaxing the constraint by a small amount — often a modest N increase (e.g., 420 → 450) produces a disproportionate improvement in study timeline.

• Proactively recommend the above-constraint N when the operational benefit is significant. If the "what-if" row shows a disproportionate improvement (e.g., 6+ months shorter study duration, or IA-FA gap dropping from borderline to comfortable), explicitly recommend it: "N = 450 exceeds the constraint by 7%, but it shortens the study by 8 months and provides a comfortable 9-month IA-FA gap vs the tight 4-month gap at N = 420. Consider relaxing the N constraint." The sensitivity table makes the trade-off visible; your recommendation makes the choice actionable. - If the user says "no constraint" or declines to specify, skip and proceed.

14. "What percentage of patients do you expect to drop out of the study per year?"

    • A) ~2% per year
    • B) ~5% per year
    • C) ~10% per year
    • D) Other

    (If co-primary endpoints) Ask dropout separately for each endpoint.

15. (Skip if no interims) "What is the minimum follow-up you want before the first interim analysis? This is the time between when the last patient enrolls and when the first IA occurs."

    • A) 3 months
    • B) 6 months
    • C) Other — specify

16. (Skip if no interims) "What is the minimum gap you want between any two consecutive analyses (including IA→FA)? This accounts for data cleaning, database lock, and review time."

    • A) 6 months
    • B) 9 months
    • C) 12 months
    • D) Other — specify

    Q15 and Q16 are minimum constraints, not fixed inputs. The actual IA and FA timing is driven by event targets (information fractions or power requirements). The min follow-up and min gap only apply as lower bounds — if the event-driven timing already satisfies them, use the event-driven timing. Only push the analysis later if the event-driven timing would violate a constraint. This prevents over-powering the design by forcing analyses later than needed.

After all inputs are collected, summarize everything in a clean table and ask the user to confirm before running any computation.

Confirmation Table Template (Single Population)

Parameter	Value
Disease / Setting	1L metastatic NSCLC
Endpoints	Co-primary PFS + OS
Randomization	1:1
Control median PFS / OS	8 mo / 20 mo
Target HR (PFS / OS)	0.69 / 0.74
Total alpha	0.025 (one-sided)
Alpha split	PFS: 0.005, OS: 0.020
Alpha reallocation	Full bidirectional
Analysis schedule	IA (PFS-triggered), FA (OS-triggered)
Alpha spending	Lan-DeMets OBF (both)
OS Power	90%
Futility	Non-binding, OS only, HSD gamma=-20
Enrollment	5/mo × 3 mo, then 20/mo
Annual dropout	PFS: 5%, OS: 2%

Confirmation Table Template (Multi-Population)

Parameter	Value
Disease / Setting	1L metastatic NSCLC
Populations	BM+ (PD-L1 TPS ≥ 50%, prevalence 50%), ITT
Endpoints	PFS + OS in both populations
Hypotheses	H1: PFS-BM+, H2: PFS-ITT, H3: OS-BM+, H4: OS-ITT
Population strategy	Step-down (subpop → overall)
Total alpha	0.025 (one-sided)
Initial alpha	H1: 0.005, H3: 0.020, H2: 0, H4: 0
Gating	H3→H4→H2; H1 tested initially
Transition matrix	H1→H3(1), H3→H4(1), H4→H1(0.5)+H2(0.5), H2→H1(1)
Randomization	1:1
Control median PFS (BM+ / ITT)	8 / 6 mo
Control median OS (BM+ / ITT)	18 / 14 mo
Target HR PFS (BM+ / ITT)	0.60 / 0.70
Target HR OS (BM+ / ITT)	0.65 / 0.75
Analysis schedule	IA1 + FA
Events per hypothesis at each analysis	[computed table]
Alpha spending	HSD gamma=-4 (all hypotheses)
Power (OS-BM+ at alpha 0.020)	90%
Futility	Non-binding, OS-ITT only
Enrollment	20/mo steady state
Annual dropout	PFS: 5%, OS: 2%

Pattern Selection

Based on the collected inputs, identify the closest design pattern from examples.md. Use the table below as a starting point — not all designs will match a pattern exactly.

Condition	Pattern	examples.md section
Single endpoint (OS, PFS, or DFS)	Pattern 1	"Single-Endpoint Design with gsSurv()"
Co-primary, alpha split, same triggers for all analyses	Pattern 2	"Co-Primary Endpoints: Alpha Splitting"
Co-primary, fixed-sequence (e.g., DFS → OS)	Pattern 3	"Co-Primary Endpoints: Fixed-Sequence Testing"
Non-proportional hazards (delayed effect)	Not a standalone pattern — see "NPH Evaluation Workflow"	Design under PH first, then evaluate under NPH
Multi-population, multi-endpoint, multi-arm	Pattern 5	"Complex Graphical Multiplicity"
Co-primary, alpha split, cross-endpoint triggers	Pattern 6	"Co-Primary Shared-Timing Iterative Workflow"
Co-primary, one endpoint tested at a single look only	Pattern 7	"Co-Primary with Single-Look Endpoint"

Single-look (k=1) endpoints: When a co-primary endpoint has only one analysis (e.g., PFS tested only at the IA), gsDesign(k=1) and gsSurv(k=1) will fail. Use compute_single_look_boundary() from examples.md and nSurv() for the baseline. See reference.md → "gsDesign(k=1) failure" for details.

Multi-population designs: Use Pattern 5 for any design with 2+ populations. Boundaries are computed via compute_gsd_boundaries() per hypothesis, and multiplicity is controlled via Maurer-Bretz graphical method. See reference.md → "Multi-Population Design" for the alpha recycling priority rules and step-down vs alpha-split strategies.

Why Schoenfeld (not gsSurv/nSurv) for subgroup events: gsSurv() and nSurv() treat the subgroup as an independent trial — they size enrollment as if the subgroup were the entire study. But in a multi-population design, the subgroup is a fraction of the total enrollment. Using gsSurv() for a 70%-prevalence subgroup effectively sizes the trial as if 100% of patients are in the subgroup, inflating the total N by up to 1/prevalence (e.g., ~43% for 70% prevalence). Instead, derive events analytically:

1. Compute required events per hypothesis: events = 4 × (z_α + z_β)² / log(HR)² (Schoenfeld)
2. For multi-look hypotheses, apply the GSD inflation factor: events_FA = events_schoenfeld × gsDesign(k, alpha, beta, sfu)$n.I[k] / gsDesign(k=1, alpha, beta)$n.I[1]
3. Compute per-patient event probability using compute_event_prob() (see examples.md), which integrates over the enrollment distribution
4. Derive N from the bottleneck hypothesis: N_sub = events_FA / event_prob, then N_total = N_sub / prevalence
5. Take the max N across all hypotheses

This approach correctly accounts for the fact that the subgroup sees only prevalence × N patients, and avoids the inflation that gsSurv() introduces.

Step-down gated hypotheses: For hypotheses with initial alpha = 0 (gated behind other hypotheses), compute boundaries at full alpha (0.025), not at the alpha that might flow through the gate. The gated hypothesis has no initial allocation — its effective alpha depends entirely on the cascade. Full alpha is the appropriate basis for design properties (boundaries, events, power).

If no pattern matches exactly, compose the design from building blocks across multiple patterns. The patterns are not mutually exclusive — real designs often combine elements. For example:

• A co-primary design with non-proportional hazards → design under PH (Pattern 6 or 7), then apply NPH Evaluation Workflow
• A single endpoint with multi-population subgroups → combine Patterns 1 + 5
• A multi-population co-primary with single-look PFS → combine Patterns 5 + 7 (see "Pattern 5+7 Combo" below)
• A 3-endpoint design with mixed fixed-sequence and alpha splitting → adapt Pattern 5

When composing, read reference.md → "Analysis Framework" for the 5-perspective approach (hypotheses, multiplicity, IA plan, boundaries, power) to structure any custom design systematically.

Pattern 5+7 Combo: Multi-Population with Single-Look PFS

This is a common pattern for aggressive diseases (e.g., 2L SCLC) with short PFS median. Uses the N-first algorithm (see reference.md → "N-First Design Algorithm"):

Phase A — Determine starting N:

1. Compute required events per hypothesis via Schoenfeld formula
2. Estimate N_min using estimate_min_N() (see examples.md)
3. Pick starting N from user's feasibility range (Q13b), close to N_min
4. Derive R from enrollment ramp (iterate K for last period)

Phase B — Design at fixed N:

1. Find IA time when PFS-ES reaches its Schoenfeld event target (PFS-triggered IA)
2. Compute OS events at IA → derive OS IF
3. Design OS boundaries with gsSurv(gamma_es, R_fixed, minfup=NULL, T=NULL) — only for boundaries, not enrollment
4. Find FA time when OS-ES reaches its required events
5. Recompute all cross-endpoint events at final IA and FA times
6. Compute single-look PFS boundaries via compute_single_look_boundary()
7. Compute OS GSD boundaries via compute_gsd_boundaries()
8. Gated hypotheses: compute boundaries at full alpha (0.025)

Phase C — Evaluate and adjust N: If power < target, timing too late, or OS IF too high → present N adjustment alongside other levers (alpha reallocation, relaxed power). Re-run Phase B.

N is a top-level design parameter. All results depend on N. Do NOT let gsSurv() determine enrollment — always fix N first, then derive everything from the fixed enrollment.

Key differences from standard Pattern 7: enrollment rates are scaled by prevalence for subgroup hypotheses, events are derived from prevalence (not nSurv()), and the multiplicity graph uses Maurer-Bretz with step-down or alpha-split gating.

Then read reference.md and the relevant sections of examples.md to proceed.

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IA Timing Checks

After computing the design (step 6), read post_design.md → "IA Timing Checks" for the full checklist, warning messages, and user options.

Quick summary — all must pass before proceeding:

• IA1 occurs at least [Q15 answer] months after enrollment ends
• Consecutive analyses are at least [Q16 answer] months apart
• Co-primary endpoint power at each analysis meets user's target

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NPH Evaluation Workflow

When the user specifies non-proportional hazards (Q7b = B), follow the "Design under PH, evaluate under NPH" approach. Do NOT size the trial directly under NPH — it breaks the IA plan.

Read reference.md → "NPH Evaluation: Design Under PH, Evaluate Under NPH" for the full rationale, step-by-step algorithm, tool selection rules, and gotchas.

Read examples.md → "NPH Evaluation with lrstat" for the complete R code pattern.

Summary of the workflow:

1. Complete the PH design (steps 1–7) — this gives events, boundaries, enrollment
2. expected_time() → AHR and timing at each PH event target under NPH
3. gs_power_npe() → analytical power under NPH
4. lrsim() → verify analyticals (timing ±1 mo, power ±2 pp, type I error ±0.5 pp)
5. Present comparison table, assessment, and options to the user

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Co-Primary Shared-Timing Design Workflow

When co-primary endpoints share analysis calendar times but different endpoints trigger different analyses, the design requires an iterative workflow because the endpoints' event counts are interdependent through the shared timeline. You cannot design both endpoints independently.

Read reference.md → "Co-Primary Shared-Timing: Iterative Design Workflow" for the step-by-step algorithm.

Read examples.md → "Co-Primary Shared-Timing Iterative Workflow" for the complete R code pattern.

Key principle: design the FA-triggering endpoint first (it drives the study size), then derive everything else from the resulting timeline.

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Verification

Every new design MUST be verified by simulation before delivery. Read post_design.md → "Verification" for the full procedure: what to verify, pass criteria, how to run lrsim(), and the verification log template.

Quick pass criteria:

• Power (H1): within ±2 pp of calculated
• Type I error (H0): within ±0.5 pp of alpha
• Events: within ±5% of calculated
• Timing: within ±1 month of calculated

Non-binding futility: use futilityBounds = rep(-6, k-1) in BOTH H0 and H1 lrsim() calls. See examples.md → "Verification with lrsim()" for the code.