Bayesian hierarchical models for adaptive randomization in ...€¦ · Ventzet al. Biometrics 2017:...

Bayesianhierarchicalmodelsforadaptiverandomizationinbiomarker-drivenstudies:

UmbrellaandplatformtrialsWilliamT.Barry,PhD

NancyandMorrisJohnLurieInvestigatorBiostatisticsandComputationalBiology

Dana-FarberCancerInstitute

Nov9th,2017

Motivationforbiomarker-driventrialsinoncology(inbrief)

• Molecularheterogeneityofcancerisnolongerahypothesis,butknown,measurable,andquantified.

Personalized/precisionmedicine: A fundamentalassumptionisthatusingthegeneticmakeupofthetumorandthegenotypeofthepatientwillenabletargetedtherapeuticstoimproveclinicaloutcomes.

• Increaseddevelopmentoftargetedtherapiesinoncology

• Componentsofmultiplexgenomicscreeningplatformsareconverging increasingoverlapping

Slide2

Motivationforbiomarker-driventrialsinoncology(inbrief)

• Manyinnovativeclinicaltrialsdesignsinoncology.Importanttodistinguishelements:

– Bayesianvsfrequentistanalysisplans

– Comparativevsnon-comparativehypotheses

– Single-stagevs.sequentialvs.continualassessment

– Adaptivevsfixedrandomization.

– Hypotheseswithinoracrossmarker-definedsubgroups

Slide3

Biomarker-drivendesignsIntegralbiomarkers- Testsinherentinthedesignfromtheonsetandmustbeperformedinrealtimefortheconductofthetrial(re:participantflow)

• Singlemarker/treatment– Enrichmentdesigns(e.g.B31/N9831)– Stratifieddesigns(TKIsandPI3KiinBr)

• Multiplemarkers/treatments– BasketandUmbrellatrial(BATTLE)– Platformtrials

• NCI-MATCH• I-SPY2

– Marker-strategydesigns(SHIVA)

Slide4

Herbst et al. Clin Cancer Res 2015;21:1514-1524

Zhouetal.(2008)ClinicalTrials5:181-193– Method(butnocode)fullyspecified

Kimetal.(2011)CancerDiscovery1:44-53– Primaryresults

BATTLE:Biomarker-integratedApproachesofTargetedTherapyforLungCancerElimination(PI:Kim)

BATTLEtrialdesign:• Hierarchicalmodel

• Bayesian(non-compara-tive)inference.

• Continualassessment

• Adaptiverandomization



• Bayesian(non-compara-tive)inference.




Kim(2011):Weplannedtorandomlyassignatleasttheinitial80patientsequallytothe4treatments,toallowatleast1patientineachmarkergrouptocompletetreatment,thusprovidingsufficientdatatoestimatethepriorprobabilityof[diseasecontrol]

Barryetal.JBS2015:TheuseofBayesianhierarchicalmodelsforadaptiverandomizationinbiomarker-drivenphaseIIstudies

Researchgoals:• EvaluatepropertiesofBATTLE

(PI:Kim),asoneofthefirstumbrellatrials

• Insilicosimulation(Rcodeasappendix)

• ContrastRARandcontinualassessmentversustraditionalSimontwo-stagedesigns






Assigned ineffective tx

Assigned effective tx






• Conclusions:• (Nearly)equalefficiency• LessvariabilityinE[N]


LessonslearnedfromBATTLE:• Challengetomakereliableassumptions

aboutprevalenceofbiomarkers

Exp.

Obs.

n≥ 1 per group n≥ 4 per group

NCI-MATCH:MolecularAnalysisforTherapyChoice

Schema of patient flow

StatisticalDesign:•1° Endpoint:• Obj resp (RECIST1.1)

• Null:5%• Target:25%

• Single-stagetest• Enroll35ptsperarm(N=31eval)• 5ormoreresp.• a =0.018• b =0.083

Protocolallowsforexpansioncohorts;notstatisticallydriven

StudyHistoryAug2015 Activatedwith 10initialdrugarmsandtargetN=3000Nov 2015 Suspendedenrollmentforplannedevaluation

795ptsregistered(739w/samplessubmitted)645ptscompletedscreening56ptswithamatchingmutation(8.7%)33ptseligibleandenrolled(5.1%)16ptsreceivedTx (2.5%)

Feb2016 Re-activatedwithaddendum#2Expandedeligibility tomyelomaIncreased toN=5000Increasedtototalof24treatmentarmsRevisedestimatewas23%ofptsmatch

Jun2017 Reached(revised) targetofN=6000pts19of26treatmentarmsstillseekingpatientsEnrollmenttosub-studiestocontinuethroughothermech’s

Slide14


http://ecog-acrin.org/nci-match-eay131


Snapshotofstudystatus(Nov2016)

IncreasedTargetN:6000pts

24genealt’nsbeingtargeted

Arm/Target Expected #Patients

Arm/Target Expected#Patients

IPIK3CAmut 137 RBRAFnonV600 29WFGFR1/2/3 124 HBRAFV600 26PPTENloss 79 TSMO/PTCH1 18Z1ANRASmut 70 UNF2loss 17S1NF1mut 66 C1METamp 14Z1D dMMR 63 AEGFR mut 8NPTEN mut 62 GROS1transloc 8QERBB2amp 59 S2GNAQ/GNA11 3BERBB2mut 39 EEGFRT790M 1C2METex14sk 37 FALKtransloc 1Z1BCCND1amp 36 XDDR2mut 0YAKT1mut 32 VcKITmut 0

http://ecog-acrin.org/nci-match-eay131


OngoingworkbyRSapigao:• Insilicosimulationofthe

dynamicaspectofaddingarmstoNCI-MATCHovertimeandreplacingcompletedarms

• Explorethepropertiesoftwo-andthree-stagedesignsinthisframework

• Add(simulated)responsesandassessBayesianmethodsforcontinualassessment.

Num

ber o

f Arm

s to

reac

h an

alys

is

04

812

1620

24

Single (N = 31)

Minimax (N1 = 21) (N2 = 31)

Optimal (N1 = 16) (N2 = 42)

3-stage (N1 = 11) (N2 = 24) (N3 = 43)




• Adaptingw/smallnjk





• Adaptingw/smallnjk• Inferencew/smallnjk

I-SPY2:Umbrella/platform(andadaptive)

ImagescourtesyofDr Rugo

Studydesign• RandomizedphaseII• Comparetoconcurrentcontrolarm(T→AC)

• 1° endpoint:pathCR• Integralbiomarkers

• HER2• HR• Mammoprint

• Bayesiananalysisplan(nextslide)

• Intendedtoallowupto4experimentalarms.

LogisticmodelforpCR

Thresholdfor‘graduation’ofaregimenafter60pts.Evidence(bypCR)thatafutureN=300phaseIIIstudywouldbepositiveinanymarker-definedsubgroup:>85%PP

Thresholdforfutilityif<10%PPinallmarker-subgroupsafter20pts.

Note:functionoftwoparameters,pe andpc

ARisproportionaltotheposteriorprob.agiventxissuperior.Priors(appeartobe)fullyspecified;dependonI-SPY1

Slide21

I-SPY2:Neoadjuvant andPersonalizedAdaptiveNovelAgentstoTreatBreastCancer

Barkeretal.Clin Pharmacol Ther.2009;86:97– 100.


• Bayesian(comparative)inference.



Rugo HSetal.NEngl JMed2016;375:23-34.


StudyHistory NCT00409968

Mar2010 Activatedwith 3initialexperimentalarms:Figitumumab,Neratinib,Veliparib +Carboplatin

Dec 2013 Resultsonthefirstregimento‘graduate’(Veliparib +Carboplatin)werereportedatSABCS byRugo etal.

Rugo HSetal.NEngl JMed2016;375:23-34Apr2014 Resultsonthe2nd regimento‘graduate’(Neratinib) were

reportedatAACR

ParkJWetal.NEngl JMed2016;375:11-22Jun2015 Resultsfora3rd regimento‘graduate’,MK-2206[AKTi],were

reportedatASCO

Jun2017 Resultsfora4th regimento‘graduate’,Pembrolizumab,werereportedatASCO


2010(TargetN =800)

2012 2014 2016(TargetN=1920)

Neratinib Ganitumab +Metformin

AMG386+Trastuzumab

PLX3397

Veliparib +Carboplatin

MK-2206+/-Trastuzumab

T-DM1andPertuzumab

Pembrolizumab

Figitumumab(droppedby2012)

Pertuzumab andTrastuzumab

Talazoparib +Irinotecan

+AMG386 Ganetespib Patritumab +/-Trastuzumab

+Conatumumab(droppedby2012)

• Nonegativearmshavebeenpublished(riskofreportingbias)• Asanongoingstudy,totalstudy-statushasneverbeen

publicallydisseminated(tomyknowledge)• Partialinformationcanbegleanedfromclinicaltrials.gov



CONSORT:Veliparib/carboplatin



Results:Veliparib/carboplatin

“…We do not report the raw data within biomarker subtypes or signatures; our analysis carries greater precision than would a raw-data estimate”


Control(T→ AC)

Enrolled N=72 N=44TNsubset N=39 N=19pCR ?? ??No pCR ?? ??


Control(T→ AC)

Enrolled N=72 N=44TNsubset N=39 N=19pCR 20 5No pCR 19 14

*imputedundersimplifiedassumptions

• MotivationandgeneralapproachofI-SPY2werepublishedwiththelaunchofthetrial(Barkeretal.2009)Insufficientdetailstoevaluatethespecificadaptivedesign.

• ConsistentwithICMJEpolicy,theprotocolwasprovidedassupplementalmaterialtotheNEJMarticles.

• Withmultipleappendices,thestatisticalmethodsappeartobespecifiedbutwillbeextremelychallengingtoreproduce.Priorsrequirepatient-leveldatafromI-SPY1.

• Softwarehasnotbeenmadepublic

• Thedecisiontoredactrawdatafrompublicationsisconcerning

• Unknownwhatthedisseminationplanswillbefornegativearms

Slide26

Commentsontransparency

Ventz etal.Biometrics2017:Bayesianresponse-adaptivedesignsforbaskettrials

Researchgoals:• Developnovelmethodstobuild

offgenomicplatforms(Dana-Farber:Oncopanel)

• ApplyRARdesigns(e.g.I-SPY2)to‘basket’trial(NCI-MATCH)

• Constructhierarchicalmodelforadaptiveallocationandcontinualassessment

• Useinsilicosimulationtotuneandevaluateproperties

• ProvidedRpackage(s)formodelsandsimulation.http://bcb.dfci.harvard.edu/~steffen/software.html

Closingremarks

Slide28

• Theuseofmasterprotocols(whetherumbrella,basket,orplatformdesigns)willcontinuetogrowfortrialswithinandacrosstraditionaldiseasetypes.

• Choiceoftrialdesigndependsonmanyparameters:

– Distributionofclinicaloutcomes,andhypothesizedtreatmenteffects

– Markerprevalence,preliminaryevidenceabiomarkerispredictive/prognostic,feasibilityofreal-timeassessment, andoperationalresources.

• Adaptivedesignsgiveflexibility,butalways atsomecost;anditmaybehardtoascertain utility

– Response-adaptiverandomizationwillbecontroversialamongstatisticians.

– Adaptiveenrichmentdesignshavethepotentialtoachievegoalsofpopulation-findingwithtargetedtherapies.

• Adaptiveplatformtrialsareforcingustorevisitoldargumentsontransparencyandwaystofacilitatethereproducibleresearch

Acknowledgements

Slide29

DukeUniversityandUNCJoeIbrahimChuckPerouLisaCareyKellyMarcom

Dana-FarberCancerInstituteSteffenVentzLorenzoTrippaGiovanniParmigianiRosemarieSapigaoMeredithReganRichardGelber

NCTNbiostatistician:DonBerryMaryRedmanBobGrey

Bayesian hierarchical models for adaptive randomization in ...€¦ · Ventzet al. Biometrics 2017:...

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