Imported from swaruplab/operon (protocols/clinical-biostatistics-adaptive-designs/SKILL.md). Install upstream with npx skills add swaruplab/operon --skill clinical-biostatistics-adaptive-designs. Copyright stays with the author.

Version Compatibility

Reference examples tested with: R rpact 4.2+ (Wassmer/Brannath), gsDesign 3.6+ and gsDesign2 1.1+ (Anderson/Merck), adaptr, simtrial. Commercial: East/EastHorizon (Cytel), ADDPLAN (ICON), FACTS (Berry Consultants).

Before using code patterns, verify installed versions match. If versions differ:

R: packageVersion('<pkg>') then ?function_name
Python adaptive packages are limited; R is the regulatory de facto standard

If code throws an error, introspect the installed package and adapt the example to match the actual API rather than retrying.

Adaptive Clinical Trial Designs

"Design an adaptive trial" -> Pre-specify a design with one or more interim adaptations (early stopping, sample-size re-estimation, treatment selection, population enrichment, randomisation ratio changes) that strongly controls Type-I error at the trial-wide level via combination tests or the Conditional Rejection Probability principle.

Regulatory Status -- The 2024-2026 Landscape

FDA 2019 Final Adaptive Designs Guidance (Federal Register 2019-25986, Dec 2 2019) finalised the 2010 and 2018 drafts. Recognises 5 design types: group-sequential, blinded SSR, unblinded SSR, adaptive enrichment, adaptive randomisation.

FDA 2022 Final Master Protocols Guidance (March 2022, NOT 2018 — common citation error): basket (one drug, many diseases), umbrella (multiple drugs, one disease), platform (perpetual, drugs enter/exit).

ICH E20 Adaptive Clinical Trials: Step 2b draft June 25 2025; Step 3 public consultation (EU deadline Nov 30 2025; FDA Federal Register Sept 30 2025); Step 4 final targeted late 2026. As of May 2026, ICH E20 is NOT final. The EFPIA/PhRMA position paper preceded the formal ICH work; Berry Consultants public comment letter is one of the more important submissions.

FDA CDER Bayesian Methodology Draft (Jan 2026) (FDA-2025-D-3217): first-ever drug-side Bayesian guidance; permits Bayesian primary inference in pivotals with simulation-based Type-I error calibration.

Project Optimus (FDA OCE, 2021-2024): rewrites Phase I/II oncology by requiring randomised dose comparison before registration, replacing MTD-and-go. Made BOIN, mTPI-2, and multi-arm dose-finding the default.

Algorithmic Taxonomy

Design type	Adaptation	Type-I preservation	Software	Strength	Fails when
Group-sequential (O'Brien-Fleming)	Early stopping for efficacy/futility	Boundary calculation; very conservative early, near-nominal at end	rpact, gsDesign	FDA's preferred adaptive design	More complex SAP; IDMC firewall essential
Group-sequential (Pocock)	Early stopping	Constant nominal alpha at each look	rpact, gsDesign	Easy early stopping	Large penalty at final analysis
Wang-Tsiatis power family	Early stopping	Parameterised by Delta	rpact	Tunable conservatism	Δ choice matters
Lan-DeMets spending function	Early stopping (flexible timing)	Alpha-spending function	rpact, gsDesign	Operational flexibility; analyses don't need pre-specified number	FDA's de facto preferred framework
Blinded SSR (Friede-Kieser 2006)	Re-estimate variance/event-rate; recompute n	No Type-I inflation; agency-uncontroversial	rpact	EMA/FDA endorsed	Variance estimate must be blinded
Unblinded SSR (Cui-Hung-Wang 1999)	Increase n based on interim effect estimate	Requires CHW weights for control; or Mehta-Pocock promising zone	rpact	Recovers power if interim promising	IDMC firewall must be perfect; Jennison-Turnbull 2015 critique
Mehta-Pocock promising zone (2011)	Increase n if conditional power in (0.3, 0.8)	Calibrated so Type-I inflation negligible (~0.001)	rpact	Operational simplicity	"Stealth alpha inflation" critique (Jennison 2015)
Bauer-Köhne 1994 combination	Combine stagewise p-values via Fisher product	Any pre-specified design modification	rpact	Most flexible; theoretical foundation	Power loss vs designed group-sequential
Müller-Schäfer 2001 CRP principle	Preserve null conditional rejection probability	Any adaptation at any time	rpact	Modern theoretical bedrock	Implementation complexity
Adaptive enrichment	Drop sub-populations failing futility	Closed-test stage-wise	rpact, adaptr	Recovers power on responders	Selection bias on enriched population
Response-adaptive randomisation	Update allocation probabilities	Stratification + time-trend covariates required	adaptr, FACTS	Patient-welfare; learn-and-confirm	Drift bias, estimator bias; controversial (Hey-Kimmelman 2015 ethics)
Bayesian platform (I-SPY 2 style)	RAR + biomarker stratification + graduation criterion	Frequentist OCs via simulation	FACTS, custom Stan/JAGS	Modern oncology adaptive	Operational complexity; requires IDMC sophistication

Postdoc reading list:

Bauer P, Köhne K 1994 Biometrics 50:1029 (combination test; original adaptive)
Cui L, Hung HMJ, Wang SJ 1999 Biometrics 55:853 (CHW weighted test for unblinded SSR)
Müller HH, Schäfer H 2001 Biometrics 57:886 (CRP principle — theoretical bedrock)
Mehta CR, Pocock SJ 2011 Stat Med 30:3267 (promising zone)
Jennison C, Turnbull BW 2015 Stat Med 34(29):3793-3810 (Mehta-Pocock critique)
Friede T, Kieser M 2006 Biom J 48:537 (blinded SSR)
Lan KKG, DeMets DL 1983 Biometrika 70:659 (alpha spending function)
O'Brien PC, Fleming TR 1979 Biometrics 35:549 (OBF boundary)
Pocock SJ 1977 Biometrika 64:191 (Pocock boundary)
Hey SP, Kimmelman J 2015 Clin Trials 12:102 (RAR ethics critique)
Berry DA 2015 commentary Clin Trials 12:107 (counter)
Robertson DS, Lee KM, López-Kolkovska BC, Villar SS 2023 Stat Sci 38:185 (canonical modern RAR review)
Wassmer G, Brannath W 2016 Group Sequential and Confirmatory Adaptive Designs in Clinical Trials (Springer)
Jennison C, Turnbull BW 2000 Group Sequential Methods with Applications to Clinical Trials (CRC)

Decision Tree by Scenario

Scenario	Recommended approach	Why
Confirmatory trial wanting interim early stopping	Group-sequential with O'Brien-Fleming boundaries via gsDesign	FDA-preferred; near-nominal final alpha
Group-sequential with flexible look timing	Lan-DeMets spending function	Operational flexibility; FDA de facto preferred
Phase 3 with uncertain nuisance parameter (variance, event rate)	Blinded SSR (Friede-Kieser)	No Type-I inflation; agency-uncontroversial
Phase 3 wanting to increase n if interim shows promise	Mehta-Pocock promising zone with CHW weights	Recovers power; calibrated Type-I
Seamless Phase 2/3 with arm selection	Bauer-Köhne combination test + closed testing	Most flexible; cite Müller-Schäfer CRP
Adaptive enrichment (drop subpopulation)	Adaptive enrichment with closed-test stage-wise	Recovers power on responders
Multi-arm oncology platform	Bayesian platform with RAR (I-SPY 2 model)	Patient-welfare argument strong for multi-arm
2-arm phase 3 oncology with potential RAR	Avoid RAR; group-sequential preferred	Hey-Kimmelman 2015 ethics + drift bias
Continuous endpoint, treatment discontinuation, follow-up data available	Hybrid: J2R imputation for treatment-discontinuation ICEs, MMRM-MAR for other missingness	Aprocitentan PRECISION precedent (2024); FDA de facto standard 2024-2025 for treatment-policy estimands
Phase 1 dose-finding	BOIN (FDA Fit-for-Purpose qualified 2021)	Transparent, tabulated decisions; no bedside Bayesian software
Phase 1b/2 dose-optimisation (Project Optimus)	Multi-arm BOIN-12 or multi-dose randomised	FDA Aug 2024 final dose-optimisation guidance
Basket trial (one drug, multiple diseases)	EXNEX or robust MAP via RBesT	Borrows across baskets while permitting one to detach
Umbrella trial (one disease, multiple drugs)	Bayesian platform with shared control	FDA Master Protocols 2022
Pediatric extrapolation borrowing from adults	Power prior with discount γ in 0.3-0.6	FDA Bayesian Jan 2026 draft endorses

Group-Sequential Designs

O'Brien-Fleming -- the regulatory default

library(gsDesign)

# OBF boundaries; 3 interim looks at 33%, 67%, 100% information
design <- gsDesign(
    k = 4,                  # total analyses including final
    test.type = 1,          # 1-sided efficacy
    alpha = 0.025,
    beta = 0.10,            # power = 0.90
    sfu = sfLDOF,           # Lan-DeMets approximation of OBF
    timing = c(0.25, 0.50, 0.75, 1.0)
)
print(design)
plot(design)

OBF is very conservative at early looks (nominal alpha approximately 0.0001 at 25% info) and near-nominal at final analysis (~0.024 of 0.025). Preferred by FDA because the final-analysis penalty is small.

Pocock -- constant nominal

Constant nominal alpha at each look. Easy early stopping but large final-analysis penalty (~0.018 of 0.025 with k=4). Rarely used in confirmatory.

Lan-DeMets spending function -- the modern flexibility

# Lan-DeMets OBF-like spending function (sfLDOF)
# Allows analysis timing to differ from pre-specified
design_flex <- gsDesign(
    k = 3,
    sfu = sfLDOF,      # OBF-like spending
    alpha = 0.025,
    beta = 0.10
)

# Actual analyses can occur at different information fractions
# Spending function returns alpha to spend at each look based on actual timing

The flexibility: sponsor can perform analyses at different information fractions than originally planned. FDA's de facto preferred framework.

Sample-size for group-sequential

# Time-to-event group-sequential
library(gsDesign)
n_gs <- gsSurv(
    k = 3,
    test.type = 2,    # 2-sided
    alpha = 0.025,
    beta = 0.10,
    sfu = sfLDOF,
    lambdaC = 0.04,   # control hazard per month
    hr = 0.70,        # treatment HR
    eta = 0.005,      # dropout hazard
    T = 24,           # total study duration
    minfup = 12       # minimum follow-up
)
print(n_gs)

Sample-Size Re-Estimation

Blinded SSR (Friede-Kieser 2006)

Re-estimate nuisance parameter (variance σ² for continuous, control event rate p_0 for binary, overall event rate for survival) from blinded interim data. No Type-I error inflation when test statistic ignores the SSR.

library(rpact)
# Blinded SSR for continuous outcome
design_blinded_ssr <- getDesignGroupSequential(
    kMax = 2,
    alpha = 0.025,
    beta = 0.20,
    sided = 1,
    informationRates = c(0.5, 1)
)

# At interim, re-estimate variance and recompute n
# (manual implementation; rpact has built-in support via getDesignInverseNormal for unblinded)

EMA Reflection Paper 2007 and FDA 2019 explicitly endorse blinded SSR. Uncontroversial.

Unblinded SSR (Cui-Hung-Wang 1999)

Interim effect estimate triggers sample-size change. Type-I inflation if naive: Cui-Hung-Wang showed 8% Type-I vs 2.5% target.

The Cui-Hung-Wang weighted test uses pre-specified weights from the original design:

Z_weighted = w_1 * Z_1 + w_2 * Z_2_residual

where w_1, w_2 are the pre-specified weights (based on original n_1, n_2) and Z_2_residual is the test statistic on the data after the interim. Pre-specified weights preserve alpha even if the actual n at stage 2 differs.

library(rpact)
design_unblinded_ssr <- getDesignInverseNormal(
    kMax = 2,
    alpha = 0.025,
    beta = 0.20,
    sided = 1,
    informationRates = c(0.5, 1),
    typeOfDesign = 'WT',  # Wang-Tsiatis power family
    deltaWT = 0.25
)

# Use inverse normal combination for adaptive SSR
analysis_result <- getAnalysisResults(
    design_unblinded_ssr,
    dataInput = getDataMeans(...)
)

Mehta-Pocock Promising Zone (2011)

At interim, compute conditional power (CP) given observed effect:

Unfavourable zone (CP < ~30%): stop or continue without modification
Promising zone (CP in 30-80%): increase n to recover power; NO Type-I penalty if increase rule pre-specified and uses original test statistic with original weights
Favourable zone (CP > 80%): continue without change

# rpact implementation
# Sample size recalculation in promising zone
n_increased <- getSampleSizeMeans(
    design_unblinded_ssr,
    alternative = 5,        # detect mean diff of 5
    stDev = 12,
    groups = 2
)

The mathematical sleight: promising zone is constructed so unconditional Type-I error inflation is negligible (~0.001) even WITHOUT CHW weighting. Jennison-Turnbull 2015 critique: stealth alpha inflation in unpublished simulation assumptions; inefficient relative to CHW-weighted GSD. Mehta defends on operational grounds.

Hsiao et al 2020 Trials 21:1003 is the systematic review.

Combination Tests and CRP Principle

Bauer-Köhne 1994 Biometrics 50:1029: combine stagewise p-values via Fisher's product test. Permits design modifications post-interim while controlling Type-I error.

Müller-Schäfer 2001 Biometrics 57:886: Conditional Rejection Probability (CRP) principle — preserve the null conditional rejection probability at every adaptation, and unconditional Type-I is preserved. The theoretical bedrock of all post-2001 confirmatory adaptive designs.

Müller-Schäfer 2004 Stat Med 23:2497 extended to ANY design change at ANY time.

# rpact natively supports combination tests
design_comb <- getDesignFisher(
    kMax = 3,
    alpha = 0.025,
    sided = 1
)

# Or inverse normal combination
design_inv_norm <- getDesignInverseNormal(
    kMax = 3,
    alpha = 0.025,
    informationRates = c(0.33, 0.67, 1.0)
)

Adaptive Enrichment

Drop sub-populations failing futility; re-power on responders. Closed-test stage-wise to control familywise error across full and enriched populations.

# rpact: enrichment design via getDesignEnrichmentSubgroup
# Standard implementation requires explicit definition of full population (F)
# and enriched population (S)

Postdoc concern: selection bias on the enriched population — the observed treatment effect on the enriched subgroup is biased upward by selection. Bias-correction via simulation or hierarchical Bayesian.

Response-Adaptive Randomisation -- The Ethics Fight

Hey & Kimmelman 2015 Clin Trials 12:102 "Are outcome-adaptive allocation trials ethical?" Argued RAR's purported ethical advantage (equipoise, sub-optimal exposure minimisation) fails in two-arm and early-phase settings because:

Drift bias inflates Type-I error / biases estimates (time trends confounded with allocation)
Consent dynamics confused — patients believe allocation is "personalised" when stochastic
Marginal patient-welfare benefit is statistical and small while operational risks real

Counter-arguments:

Berry DA 2015 commentary Clin Trials 12:107: RAR enables learn-and-confirm, multi-arm platforms (I-SPY 2 model) where the patient-welfare argument IS the point and equal allocation would be unethical given accumulating evidence.
Saville & Berry 2016 Clin Trials 13:358: RAR's operating characteristics are competitive in multi-arm; bias and inflation problems disappear with stratification, time-trend covariates, and proper analysis weights.
Buyse 2015 Clin Trials 12:108: Hey-Kimmelman correct for 2-arm but wrong for multi-arm.

Consensus position (2020s; ICH E20): RAR appropriate when (a) multi-arm (>=3 arms), (b) rare disease / limited pool, (c) strong PoC of differential biomarker response, (d) robust drift-bias adjustment and pre-specified analysis weights. Inappropriate for confirmatory 2-arm trials.

Robertson, Lee, López-Kolkovska, Villar 2023 Stat Sci 38:185 ("Response-adaptive randomization: from myths to practical considerations") is the canonical modern review settling the debate.

Bayesian Platform Trials

I-SPY 2 (Barker-Sigman 2009 Clin Pharmacol Ther; Park-Liu 2016 NEJM 375:11): neoadjuvant breast cancer; 10 biomarker-defined subtypes × multiple arms; Bayesian RAR; graduation criterion = posterior predictive probability of success in 300-patient Phase 3 ≥ 85%. Berry Consultants designed the engine. Multiple drugs graduated (neratinib, veliparib, pembrolizumab).

GBM AGILE (Alexander 2018; published readouts beginning 2024): glioblastoma; response-adaptive Bayesian; first global registrational platform in neuro-oncology. Regorafenib readout 2025 JCO JCO-25-01137.

REMAP-CAP (Angus 2020 JAMA): severe pneumonia, repurposed for COVID-19 in 2020; Bayesian factorial multi-domain design — multiple intervention domains tested simultaneously and combinatorially. Generated corticosteroid signal in COVID independently of RECOVERY.

Drop-the-loser vs promising-the-winner

Adaptive arm-dropping (futility): Bayesian posterior probability of beating control drops below threshold -> arm closes. Mathematically straightforward; FDA-acceptable.
"Promising-the-winner" (graduate to Phase 3): introduces selection bias. Bias-adjusted estimators (Robertson 2023; conditional MLE) now standard in I-SPY 2 reports.

Phase I Dose-Finding -- BOIN, mTPI, CRM

Design	Citation	Idea	Where it wins
CRM	O'Quigley-Pepe-Fisher 1990	Single-parameter logistic/power model; updates posterior MTD probability after each cohort	Statistically efficient; skeleton calibration needed
EWOC	Babb-Rogatko-Zacks 1998	CRM-like with explicit overdose-control constraint (P(dose > MTD) <= 0.25)	Safer than CRM in small trials
mTPI	Ji et al 2010 Clin Trials 7:653	Beta-binomial; UPM decision rule on under/proper/over-dosing intervals	Pre-tabulated decisions; documented over-shoot bias
mTPI-2 / Keyboard	Guo-Wang-Chen-Ji 2017	Fixes mTPI Ockham bias by equal-width intervals	Default mTPI replacement
BOIN	Liu-Yuan 2015 J R Stat Soc C 64:507	Pre-tabulated escalation interval bounds optimised to minimise incorrect-decision probability	FDA Fit-for-Purpose qualified Dec 2021; near-CRM with no bedside software

Why FDA prefers BOIN operationally: qualified as Fit-for-Purpose under FDA Drug Development Tools program (review document FDA-2020-X-XXXX, posted 2021). Investigator uses pre-printed escalation table — no real-time Bayesian software at the bedside.

R packages: BOIN, dfcrm (Cheung — author of CRM textbook), trialr (Brock — includes EffTox), escalation (Brock — unified framework).

Reconciliation: When Methods Disagree

Pattern	Likely cause	Action
Blinded SSR n vs unblinded SSR n differ substantially	Unblinded SSR uses interim effect estimate; blinded uses nuisance parameter only	Blinded is Type-I-clean; unblinded requires CHW weighting; pre-specify the approach in SAP
Group-sequential rejects at interim; Cui-Hung-Wang weighted final test does not	Naive interim rejection used original test statistic; CHW weights downweight late data	Pre-specify boundary and weights; do NOT switch tests mid-stream
Mehta-Pocock promising-zone vs CHW-weighted GSD give different n increases	Promising zone calibrated for Type-I (~0.001 inflation); CHW more efficient under known effect	Jennison-Turnbull 2015 critique: promising zone "stealth alpha"; pre-specify with simulation OCs
Adaptive enrichment selects subgroup at interim; replication shows smaller effect	Selection bias on enriched population (Sun 2010 winner's curse)	Bias-correction via conditional MLE or hierarchical Bayesian; cite Robertson 2023
RAR posterior allocation favours active in 2-arm trial; randomisation drift bias suspected	Time trends confounded with allocation changes	Pre-specify time-trend covariates in analysis; use proper analysis weights; cite Robertson 2023 RAR consensus (RAR INAPPROPRIATE for 2-arm confirmatory)
BOIN vs CRM choose different MTD on same data	CRM uses model; BOIN uses tabulated boundaries; differ when skeleton mis-calibrated	BOIN Fit-for-Purpose qualified (Dec 2021); CRM more efficient under correct skeleton; report OCs over both
I-SPY 2 graduation criterion met but Phase 3 replication fails	Selection bias on graduated arm; PP threshold not bias-corrected	Apply conditional MLE; cite Robertson 2023; report both raw and bias-corrected estimates
Müller-Schäfer CRP preserved but ad hoc rule appears Type-I-inflated in simulation	Implementation deviation from formal CRP	Verify CRP equation precisely; report OCs via simulation; cite Müller-Schäfer 2001

Per-Method Failure Modes

Unblinded SSR with naive sample increase

Trigger: Sponsor increases n based on interim effect without CHW weighting.
Mechanism: Type-I inflation up to 8% (Cui-Hung-Wang 1999 simulation).
Symptom: Independent reanalysis finds Type-I > 5%.
Fix: Pre-specify CHW weights from original design; use combination test in rpact.

Mehta-Pocock promising-zone "stealth alpha"

Trigger: Promising-zone applied without sufficient simulation.
Mechanism: Jennison-Turnbull 2015 critique — Type-I inflation hidden in unpublished simulation assumptions.
Symptom: Independent reanalysis finds Type-I 5.3% vs nominal 5%.
Fix: Pre-specify increase rule transparently; report simulation OCs.

RAR drift bias

Trigger: RAR in trial with time trends (calendar effects, learning curves).
Mechanism: Time trends confounded with allocation changes; biased effect estimate.
Symptom: Effect estimate sensitive to time-trend adjustment.
Fix: Pre-specify time-trend covariates in analysis; use proper analysis weights; cite Robertson 2023.

Schoenfeld formula under immunotherapy delayed effect

Trigger: Sample size calculated via Schoenfeld 1981 assuming PH.
Mechanism: Delayed effect violates PH; events under-estimated by 20-50%.
Symptom: Trial under-powered; observed events insufficient.
Fix: Lakatos 1988 or simulation under expected HR(t); cite Lin 2020 NPH Working Group.

IDMC firewall failure in unblinded SSR (IDMC = Independent Data Monitoring Committee; the regulatory-standard term)

Trigger: Interim effect estimate leaks beyond IDMC.
Mechanism: Sponsor inference from increase decision reveals direction of interim effect.
Symptom: Regulator audit reveals unblinding.
Fix: Strict firewall SOP; only "increase / no increase" communicated to sponsor; cite ICH E20.

Adaptive enrichment selection bias

Trigger: Enriched population effect reported without bias correction.
Mechanism: Selection on subgroup with promising interim effect inflates estimate.
Symptom: Independent replication on enriched subgroup gives smaller effect.
Fix: Bias-correction via simulation or hierarchical Bayesian; cite Sun 2010 winner's curse.

RAR in 2-arm confirmatory

Trigger: RAR applied to confirmatory 2-arm trial.
Mechanism: Hey-Kimmelman 2015 critique — drift bias, consent confusion, marginal benefit.
Symptom: Reviewer rejects RAR as inappropriate for setting.
Fix: Group-sequential with futility/efficacy boundaries instead; cite Robertson 2023 consensus.

CRM skeleton mis-calibration

Trigger: CRM applied with default skeleton without simulation.
Mechanism: Skeleton dictates target dose; mis-calibration biases MTD.
Symptom: MTD selection differs systematically from clinical expectation.
Fix: Calibrate skeleton via Lee-Cheung 2009 indifference-interval method; or switch to BOIN.

Quantitative Thresholds

Threshold	Source	Rationale
FDA Fit-for-Purpose BOIN qualification (Dec 2021)	FDA Drug Development Tools program	First dose-finding design with formal FDA endorsement
Mehta-Pocock promising zone CP 30-80%	Mehta-Pocock 2011	Mathematical calibration for Type-I preservation
RAR appropriate >= 3 arms	Robertson 2023 consensus	Multi-arm patient-welfare argument
OBF nominal alpha ~0.024 at final / 0.025	gsDesign	Small final penalty preferred by FDA
Schoenfeld under non-PH under-estimates 20-50%	Lin 2020 NPH WG	Use Lakatos or simulation
I-SPY 2 graduation: PP success in Phase 3 >= 85%	Barker 2009	Bayesian platform standard
Power prior discount γ 0.3-0.6 for pediatric extrapolation	FDA Bayesian Jan 2026 draft	Partial borrowing default

Common Errors

Error / symptom	Cause	Solution
Unblinded SSR with naive sample increase	No CHW weighting	Pre-specify CHW weights; cite Cui-Hung-Wang 1999
Mehta-Pocock without simulation OCs	Stealth alpha inflation	Report simulation OCs; cite Jennison 2015
RAR in 2-arm confirmatory	Misapplication	Group-sequential instead; cite Robertson 2023
Schoenfeld for immuno-oncology	PH assumption violated	Lakatos or simulation; cite Lin 2020
Adaptive enrichment effect reported uncorrected	Selection bias	Bias-correction; cite Sun 2010
CRM with default skeleton	Mis-calibration	Calibrate via Lee-Cheung 2009 or switch to BOIN
ICH E20 cited as "finalised April 2024"	Confusion with EFPIA position paper	ICH E20 is Step 2b/3 draft (June 2025); not final
FDA Master Protocols "2018"	2018 was draft	March 2022 was the final
Bauer-Köhne combination test as "old-fashioned"	Misunderstanding	Foundational; cited in modern combination-test implementations
Stop-for-efficacy at first interim with OBF	OBF nominal alpha ~0.0001 at 25% info	Trial must show very strong evidence to stop early; expected

Anticipated Reviewer Pushback

Pushback	Response
"How is Type-I error controlled?"	Closed testing via Müller-Schäfer CRP principle; specific implementation is inverse normal combination test in rpact
"Why these boundaries?"	OBF via Lan-DeMets sfLDOF spending function; preserves final-analysis power; pre-specified in SAP
"Pre-specification of SSR rule?"	Promising zone CP in (0.3, 0.8) triggers increase to n_max via CHW-weighted statistic; pre-specified n_max in SAP
"IDMC firewall?"	IDMC receives interim effect estimate; sponsor receives only "increase / no increase" decision; SOP documented; pre-specified
"RAR ethics?"	Multi-arm (4 arms) setting; Berry 2015 consensus that patient-welfare argument valid; drift-bias adjustment in primary analysis
"Promising zone vs CHW-weighted GSD?"	Operational simplicity preferred; OCs from simulation confirm Type-I ~5%; supportive Cui-Hung-Wang analysis
"Adaptive enrichment bias?"	Bias-correction via simulation; conditional MLE for enriched-population effect; cite Sun 2010
"Phase 1 BOIN vs CRM?"	BOIN Fit-for-Purpose qualified by FDA Dec 2021; tabulated decisions; no bedside Bayesian software

References

Babb J, Rogatko A, Zacks S. 1998. Cancer Phase I clinical trials: efficient dose escalation with overdose control. Stat Med 17:1103-1120.
Bauer P, Köhne K. 1994. Evaluation of experiments with adaptive interim analyses. Biometrics 50:1029-1041.
Berry DA. 2015. Commentary on Hey & Kimmelman. Clin Trials 12:107-110.
Cui L, Hung HMJ, Wang SJ. 1999. Modification of sample size in group sequential clinical trials. Biometrics 55:853-857.
FDA. 2019. Adaptive Designs for Clinical Trials of Drugs and Biologics. Final Guidance.
FDA. 2022. Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics. Final Guidance, March 2022.
FDA. 2026. Use of Bayesian Methodology in Clinical Trials. Draft Guidance, January 2026.
Friede T, Kieser M. 2006. Sample size recalculation in internal pilot study designs. Biom J 48:537-555.
Hey SP, Kimmelman J. 2015. Are outcome-adaptive allocation trials ethical? Clin Trials 12:102-106.
Jennison C, Turnbull BW. 2015. Adaptive sample size modification in clinical trials: start small then ask for more? Stat Med 34(29):3793-3810.
Lan KKG, DeMets DL. 1983. Discrete sequential boundaries for clinical trials. Biometrika 70:659-663.
Liu S, Yuan Y. 2015. Bayesian optimal interval designs for phase I clinical trials. JRSS-C 64:507-523.
Mehta CR, Pocock SJ. 2011. Adaptive increase in sample size when interim results are promising. Stat Med 30:3267-3284.
Müller HH, Schäfer H. 2001. Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and of classical group sequential approaches. Biometrics 57:886-891.
O'Brien PC, Fleming TR. 1979. A multiple testing procedure for clinical trials. Biometrics 35:549-556.
O'Quigley J, Pepe M, Fisher L. 1990. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics 46:33-48.
Pocock SJ. 1977. Group sequential methods in the design and analysis of clinical trials. Biometrika 64:191-199.
Robertson DS, Lee KM, López-Kolkovska BC, Villar SS. 2023. Response-adaptive randomization in clinical trials: from myths to practical considerations. Stat Sci 38:185-208.
Wassmer G, Brannath W. 2016. Group Sequential and Confirmatory Adaptive Designs in Clinical Trials. Springer.

Related Skills

clinical-biostatistics/power-and-sample-size - Sample size for adaptive designs
clinical-biostatistics/multiplicity-graphical - Closed testing in adaptive contexts
clinical-biostatistics/bayesian-trials - Bayesian platform trials, BOIN/CRM/EWOC
clinical-biostatistics/trial-reporting - Reporting adaptive trial results per CONSORT 2025
clinical-biostatistics/survival-analysis - Adaptive designs for TTE endpoints
experimental-design/sample-size - General sample-size methods

clinical-biostatistics-adaptive-designs

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