choosing monitoring boundaries: balancing risks and benefits · 19/4/2017 · boundaries than...
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ChoosingMonitoringBoundaries:BalancingRisksandBenefits
DepartmentofBiostatistics,EpidemiologyandInformaticsUniversityofPennsylvania
April19,2017
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Outline
• Whatdowemonitor?• Howdowemonitorit?• Achallengingexample• Somenewproposals• Discussion
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Whatdowemonitor?• Clinicaltrialarchitectureistypicallydefinedbyaprimaryefficacyoutcome
• A fundamentalroleofaDSMBistoassessthebenefit/riskratio
• Priorstudiescanyieldalistofpotentialrisks/benefits– Maybesymptoms(nausea,pain,etc.)ormayberisksofsevereoutcomes(elevatedstroke,cancer,death)
– Mayalsohaveimportantsecondaryefficacyendpoints(e.g.fracturesinWHIHormoneReplacementTrial)
– Trialsstructurealsosetuptocaptureunanticipatedadverseevents
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Someofthechallengestoassessingbenefit/risk
1. Multivariateoutcomesneedtobeconsidered– Theseoutcomesmaybeofvaryingseverity
2. Risksmaychangeovertime3. Risksmaybeinfrequent/rare4. Fornoveltherapies,risksmaybelargelyunknown5. Expecttheunexpected…1&2implythatinordertoevaluaterisk/benefitonehastoprioritizetheoutcomesandprioritizetheimportanceofearly/lateevents(explicitlyorimplicitly,formallyorinformally)
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Twoapproachesformonitoringrisk/benefit
1. Multipleoutcomesassessedseparately– Primaryendpointmayhaveaformalmonitoringboundary– DSMBispresentedwithanalysesofseveralseparateendpoints:primary,key2nd-ry,importantsafetyoutcomes
– DSMBweighstotalityofevidence,asubjectivejudgmentismadeforoverallbalanceofrisk/benefit
2. Astatisticsummarizingrisk/benefitisassessed– Compositeendpointdeterminedpriortostartoftrial– Risk/benefitp-valuecalculated/comparedtoaboundary– Subjectivejudgmentstillneededtoweighttotalityofevidence
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Issuesthatcomplicateevaluationofthebenefit/risktradeoff
• Severityofhealthoutcomesaffectedbythetreatmentmaybeverydifferent– Assessingoverallbenefitmeansgivingrelativeweightstothese
risks/benefits– Patients/cliniciansmayhavedifferingopinionsontheseweights
• Frequencyofhealthoutcomesaffectedbythetreatmentmaybeverydifferent– Whendoestheincreasedriskofarare,butserioussideeffect
offsetthebenefitofatreatment?• Toleranceofasideeffectdependsonwhetheritisina
healthypopulationorsickpopulation• Timingofendpointsmaydiffer:earlyharm,laterbenefitor
viceversa6
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WHIExample• Women’sHealthInitiative(WHI)conductedtwohormonetherapy(HT)trials
• TrialswereuniqueintheamountofdatacollectedonHTpriortothetrialstart– Expecting40-50%decreaseinheartattacks– Observationalstudiesraisedconcernoverincreaseinbreastcancer
• AformalmonitoringplanwasputintoplaceforbothefficacyandharmforbothHTtrials– Considered8outcomesofroughlyequalimportance.Mostthoughttoberelatedtoefficacy
– Hadaglobalindexofbenefit/risk(Z=-1)(Wittes etal.2007;Freedmanetal.1996) 7
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WHIHTmonitoringplan
• Primaryefficacyendpoint:Coronaryheartdisease(CHD)
• Primarysafetyendpoint:invasivebreastcancer• Formalanalysesusedweightedlog-rankstatistictofurtherdown-weightearlyevents– MotivatedbyexpectedearlyCHDbenefitandlateBCAharm.Also,drugneededtimetohaveaneffect
– Unweightedusefulincasetherewereearlyharms,don’twanttodownweightthem
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WHITrial:Unexpectedoutcomes• Discrepancybetweenexpectedandobservedefficacyandsafetyendpoints– Earlyon,anincreasedriskofCHD/stroke/PEforactivearmemergedinbothtrials
– Lateron,divergenteffectsappearedforbreastcancer
• Debateensuedwhetherandhowthesafetyendpointbemodified(Wittes etal2007)
• Levelofsignificanceanddirectionofeffectvariedbasedonweightedvsunweightedlog-rankstatistics
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WHIExample:Lessonslearned/affirmed
• Monitoringmultivariateoutcomesiscomplex• Reliablyassessingriskandharmsmeansknowingwhichendpointsarewhich
• Difficulttorelyonasinglep-valuewhenconsideringamultivariateoutcome
• Decision-makingisultimatelyasubjectiveactivity
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WHIexamplehighlightsmonitoringduality• Pre-specifiedboundariesprotectagainstinflatingp-values
bydefiningriskcategoriesafteradifferenceisobserved• Formalboundarieshowevercan”lockthinking”andneed
tobeflexibleinthefaceofunexpectedrisks– Adesiretosticktopre-specifiedboundaries– Ironically,statisticianscanbequickertoditchtheboundariesthanclinicalcolleagues
• Desiretohaveaclear,data-drivenstatisticdothework,butinterpretationneedstobringinaglobalperspective– Datafromothertrials– Leaningsofothertrendsindata– Uncertaintyinassumptionsbehindmonitoringboundary
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Needforbetterstatisticalapproachestoassessbenefit/risk?
Usualstatisticalapproacheshavesomelimitations:• Timetofirstignoressubsequentandpotentiallymoresevereoccurringendpoints
• HRcanoveremphasizeincreasesinsmallabsoluterisk
• HR–precisionlimitedbynumberofevents• Unoetal.2015discussadvantagesofriskdifference,percentiledifference,restrictedmeansurvivaldifferenceinnon-inferioritytrials
• Multiplicity
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Manyrecentproposalsforassessingbenefit/risk*
–Win-ratio:Pocock etal.2012,FinkelsteinandSchoenfeld 1999;Bebu andLachin 2016,Oakes2016
– Severityranking:ShawandFay2016– TotalAssessment:Evansetal2015(DOOR),Berryetal.2013
– OutcomeWeighting:Bakal etal.2013– Proportionfavoringtreatment:Buyse 2010– Jointtest:FinkelsteinandSchoenfeld 2014
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Approachestoassessingbenefit/risk
1. Createanaggregatescorefromaweightedsumofoutcomes– Interpretedasaglobalassessmentofpatientoutcome– Naturallyincorporatesmultipleevents
2. Orderoutcomesintermsofapreferredimportanceandrank/classifypatientsusingthehighestorderedoutcomepossible– Forcensoredeventtimesoftenmeansrankingpatientsoveracommonfollow-uptime
– Essentiallycreatesaweightedcombinationofscorestatistics,wheretheratesrelatetotheprobabilityoftheeventsofhigherorderbeingobserved
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Differingopinionsonwhethertocreateseparatesafetyandefficacycomposites
• EvansandFollmann 2016advocateaunifiedcompositeofbenefitandriskasapragmaticendpointofeffectiveness
• Kipetal2008recommendagainstlumpingsafetyandefficacylimitsinterpretabilityinsettingofcardiovasculardisease• Frequentlydominatedbyasubclassofendpoints• Toosusceptibletoprovidingmisleadingevidence
• “Althoughnumerousapproachesandframeworkshavebeenproposedinrecentyears,thereisnosingleapproachorframeworkthatcanbeappliedandutilizedineverysetting.”(Ch 8,Jiang,He2016)
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Winratio1
• Patientsintreatmentandcontrolgroupsareplacedintomatchedpairsaccordingtotheirriskprofiles
• Determineprioritizationofoutcomes– Example:twoendpoints:deathorMIHospitalization,consider
timetodeathfirstthentimetohospitalization• Withineachpair,atx subjectislabeledawinnerorloser
usingtheoutcomeofhighestprioritypossible– Comparetimetodeathifpossible;otherwisecomparetimeto
hospitalization;otherwisetied• Thewinratioistheratioofwins/lossfortreatmentarm
– P-valueandCIarereadilyobtainable
1.Pocock 201216
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Usefulfeaturesofwinratio
• Canconsiderallobservedeventsonapatient– Allowsmoresevereeventstohavehigherpriority– Particularlyusefulincaseswherefirsteventisexpectedtobethelesssevereevent
• Potentiallyhigherpowerthananysingleendpoint– Particularlyiftreatmenteffectsimilaracrossendpoints
• Easytocalculateandmakeinference1,2,3
– UnpairedversionisavailableusingaU-statisticderivedfromallpossibletx-controlpair
171. Pocock 20122.Bebu andLachin,2016;3.Oakes2016
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Winratioexample:TheSOLVDtrial(NEJM1991)
Background• SOLVDincludedaRCTofanoveltreatmentforpreventionof
mortality/hospitalizationinpatientswithcongestiveheartfailure(CHF)andweakleftventricleejectionfraction(EF)
• In1986-89,2569patientsrandomizedtoenalapril orplacebo
• Enalapril foundbeneficialformortality(p=0.0036)andtimetofirsthospitalization/death(p<0.0001)
Analysis• Consideredasubsetof662diabeticsubjects• Computeusualtime-to-first(TTF)endpoint• Computewinratioforcontrol-treatmentpatientspairs
formedusingabaselineCoxmodelriskscorefordeath18
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SOLVDTrial:Time-to-firstanalysis
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SOLVD:Winratio
• 343onPlaceboarm,319onactivearm– 24patientsgounusedinthepairedanalysis
• 145winsonactive;112winsonplacebo• WR=145/112=1.29(p=0.038)– 189rankedondeath:98winsforactive,91winsforplacebo
– 68rankedonhospitalization:47winsactive;21winsplacebo
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Afewkeypointsaboutwinratio
• TheparametertheWRestimatesdependsonthecensoringdistributionsoftheendpoint– Importantconsiderationifearlyandlaterisks
• Trialsofdifferentlengthswillgenerallybeestimatingadifferenteffectestimate
• Whenpatientshavevaryingfollow-uplengthstheWRbecomesmoredifficulttointerpret– SOLVDfollow-up:1dayto4.6yearsinexample
• Ifdeathdeterminesseverity,thenisrankingbyotherlesssevereendpointsgaininginformationorameansofpotentialmisclassification?
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Winratio:Gaininginformationfromhospitalizationormisclassifying?
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HH
Censoringtime
Patient1diedat3years;Patient2censoredat2.5years;diedat4years
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WinRatio:Gaininginformationfromhospitalizationormisclassifying?
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HH
Censoringtime
Patient1diedat3years;Patient2censoredat2.5years;diedat4years
Thetruestateofinformationhereisthatthepatient1severityrelativetopatient2isintervalcensored.
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ClinicalseverityrankingShawandFaySIM2016
• Rankindividualsaccordingtoclinicalseverity,usinginformationonboththesurrogateandtrueendpoint– Rankingfunctionofthetwoeventtimescanvarybysetting
• Settingofinterest:XDR-TB:sputumconversion/death– Rankpatientsbytimeofdeathifobserved
• Earlierisworse
– Ranktimetosputumconversionforthesurvivors• Earlierisbetter
– Conversiontimeirrelevantifpatientlaterdies• Performtwosampletestonaninterval-censoredclinicalseveritywhichincorporatesbivariatesurvivalinformation
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ShawandFaySIM2016Ranking Values: Worst to Best
Time to Surrogate
Tim
e to
Dea
th
0 5 10 15 20 ∞∞
05
1015
20
12 23 3 34 4 4 45 5 5 5 56 6 6 6 6 67 7 7 7 7 7 78 8 8 8 8 8 8 89 9 9 9 9 9 9 9 9
10 10 10 10 10 10 10 10 10 1011 11 11 11 11 11 11 11 11 11 1112 12 12 12 12 12 12 12 12 12 12 1213 13 13 13 13 13 13 13 13 13 13 13 1314 14 14 14 14 14 14 14 14 14 14 14 14 1415 15 15 15 15 15 15 15 15 15 15 15 15 15 1516 16 16 16 16 16 16 16 16 16 16 16 16 16 16 1617 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 1718 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 1819 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 1920 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 2041 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21
1234567891011121314151617181920
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Greybox:Severityscoreforpatientwhoconvertedinweeks6-8,inclusive,butdroppedoutafterweek16.Intervalcensoringindisjointintervals
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Furthermusingsontestsofseverityusingjointsurvivaldistribution
• Takeadvantageofalltheinformationregardingthesurvivaltime(notlimitedtocommonfollow-uptimesforpairs)
• Includingthetrueuncertaintyabouttheseverityofapatient
• Teststatisticforcompositestillhastheproblemthatthatparameterestimateddependsonlengthoftrial– FayandShawshowedthattheresultingteststatisticisaweightedsumofateststatisticondeathandonthesurrogate
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DOORRankingEvansetal.2015
• Collectionofpossibilitiesofclinicaloutcomesofapatientsarerankedaccordingtopreferredtoleastpreferredoutcome– Rateallpossibleclinicalpathsonanordinalscale– Rating/rankingcanbedonebyexpertclinicalpanel,potentially
alsoincludingpatients– ThenaU-typestatisticcouldbeusedtoexamineiftheoutcome
ontx betterthanthatforapatient• Proposedablindedadjudicatedcommitteecouldevaluate
clinicalseveritybasedonpatientchart– Notpracticalforlargertrialsorveryreproducible
• Similarideasdiscussedbyanumberofauthors,includingChuang-Steinetal.1991
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DOORhypotheticalexample(Evansetal.2015)
1. ClinicalbenefitwithoutAE2. ClinicalbenefitwithAE3. Survivalw/oclinicalbenefitorAE4. Survivalw/oclinicalbenefit+AE5. Death• Insettingofanti-infective,breaktiesusinglengthofantibioticregimen(DOOR/RADAR)
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DOOR:AdvantagesandlimitationsAdvantages– Simpleandintuitivemeasure– Rankingcognitivelyeasierthanweighting– Canincorporatedifferentrankingsystems
Limitations– Varyinglengthoffollow-upcanbeachallenge– Lossofinformationthroughtiescanbeaproblemforordinal
– Willbedifficulttoadapttounexpectedbenefitsorrisks.Wouldneedtoreconveneoutcomerankingpanel
• Perhapsbestusedalong-sideIndividualcomponentsforinterpretation
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Somepragmaticconsiderations• Forcompositeendpoints:creategroup(s)basedonsimilar
severity– Somesettingsmaywanttopoolsafetyandriskfornetclinical
benefit– Addedinterpretabilityifindividualsoutcomesoccurwithsimilar
frequency• Sensitivityanalysistoseeimpactofvaluesystems
– Ifusingoutcomeweighting,canbeusedidentifythe“valuebreakingpoint”
• PracticeRundecisionscenarios:Valuableexercisetohonetheneededvaluejudgements(somecanbepre-specified)andstatisticaldecisionboundaries
• Clearpresentationandvisualizationofdata(estimates)forDSMBreportwillaidinassessmentoftotalityofevidence
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Conclusions• Nooneapproachwillworkforeverysetting• Goodtorememberallapproachesinvolvesubjectivity• Specificendpoint+compositesthatsummarizeeffectonmultipleendpointsseemslikeaflexibleandpowerfulcombination
• Statisticalpropertiesofcomposites needrigorousexaminationandthoroughnumericalinvestigationbeforestartoftrialforexpectedscenarios
• Practicerundecisionscenarios:Valuableexercisetotohonetheneededvaluejudgementsandstatisticaldecisionboundaries
• Apriordevelopmentofrisk-benefitstatisticandboundaryisausefuldecisiontoolbutcannotbeprescriptive
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Thankyou!
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