Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.
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Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.
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 expected in 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)
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 hazardT=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.
Edwards et al 2020 Trials 21:1000 is the systematic review.
Combination Tests and CRP Principle
Bauer-Köhne 1994Biometrics 50:1029: combine stagewise p-values via Fisher's product test. Permits design modifications post-interim while controlling Type-I error.
Müller-Schäfer 2001Biometrics 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.
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
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 commentaryClin 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 platforms. Drift bias and Type-I inflation are controlled by adjusting for temporal trends (time-trend covariates) alongside stratification and proper analysis weights -- the "Bayesian time machine" approach developed in later work.
Buyse 2015 Clin Trials 12:119: 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
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's Drug Development Tools program (FDA Determination Letter, December 10, 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 (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)
Bias-correction via simulation; conditional MLE for enriched-population effect; cite Robertson 2023
"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 12:107-109.
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
