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MarginalStructuralModelsforEs3ma3ngtheEffectsof
ChronicCommunityViolenceExposureonAggression&Depression
TraciM.Kennedy,PhDTheUniversityofPi0sburgh,DepartmentofPsychiatry
EdwardH.Kennedy,PhDCarnegieMellonUniversity,DepartmentofSta>s>cs
ModernModelingMethodsConferenceMay23,2017
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Today’sTalk
1. Communityviolenceexposure
2. Causalinference&marginalstructuralmodels(MSM)
3. ApplicaDon
4. Results
5. Discussion
6. Resources
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CommunityViolenceExposure&YouthMentalHealth
• High prevalence of youth CVE in U.S. cities • Range of outcomes
• Internalizing symptoms • Depression, anxiety, PTSD
• Externalizing symptoms • Aggression, delinquency
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CommunityViolenceExposure&YouthMentalHealth
• CumulaDveeffectsmodel– Dose-response– Linear– Supportedinliterature
• ButMOSTstudiestestonlylineareffects
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CommunityViolenceExposure&YouthMentalHealth
• CumulaDveeffectsmodel– Dose-response– Linear– Supportedinliterature
• ButMOSTstudiestestonlylineareffects
CVE
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CommunityViolenceExposure&YouthMentalHealth
• CumulaDveeffectsmodel– Dose-response– Linear– Supportedinliterature
• ButMOSTstudiestestonlylineareffects
• DesensiDzaDonmodel:curvilinear– IsolatedCVE!internalizing– ChronicCVE!externalizing
CVE
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CommunityViolenceExposure&YouthMentalHealth
• CumulaDveeffectsmodel– Dose-response– Linear– Supportedinliterature
• ButMOSTstudiestestonlylineareffects
• DesensiDzaDonmodel:curvilinear– IsolatedCVE!internalizing– ChronicCVE!externalizing
CVE
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Ext
Int
CVEW
ell-b
eing
Ext
Int
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CommunityViolenceExposure&YouthMentalHealth
• CumulaDveeffectsmodel– Dose-response– Linear– Supportedinliterature
• ButMOSTstudiestestonlylineareffects
• DesensiDzaDonmodel:curvilinear– IsolatedCVE!internalizing– ChronicCVE!externalizing– “pathologicadaptaDon”
CVE
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CVEW
ell-b
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Ext
Int
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CommunityViolenceExposure&CausalInference
• AssociaDons– CVE!symptoms
– ObservaDonal– CorrelaDon≠CausaDon
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
A!Y
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure
Y1!A1
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure
Y1!A1!Y2
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure– Violenceexposurecausesaggression,which
causesmoreviolenceexposure,etc.
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure– Violenceexposurecausesaggression,which
causesmoreviolenceexposure,etc.
A1!Y1!A2!Y2
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure– Violenceexposurecausesaggression,which
causesmoreviolenceexposure,etc.
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure– Violenceexposurecausesaggression,which
causesmoreviolenceexposure,etc.
– Somethingelse(SES,neighborhood)causesboth
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CommunityViolenceExposure&CausalInference
• PossibleexplanaDons– Violenceexposurecausesincreasedaggression
– Aggressioncausesincreasedviolenceexposure– Violenceexposurecausesaggression,which
causesmoreviolenceexposure,etc.
– Somethingelse(SES,neighborhood)causesboth
L
A1!Y1!A2!Y2
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Wewanttocomparewhatwouldhavehappenedunder
noexposure(“treatment”)towhatactuallyhappenedtosomeonewhowasexposed(“treated”)
• E(Y1–Y0)
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Wewanttocomparewhatwouldhavehappenedunder
noexposure(“treatment”)towhatactuallyhappenedtosomeonewhowasexposed(“treated”)
• E(Y1–Y0)
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Wewanttocomparewhatwouldhavehappenedunder
noexposure(“treatment”)towhatactuallyhappenedtosomeonewhowasexposed(“treated”)
• E(Y1–Y0)
Whatactuallyhappened:
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Wewanttocomparewhatwouldhavehappenedunder
noexposure(“treatment”)towhatactuallyhappenedtosomeonewhowasexposed(“treated”)
• E(Y1–Y0)
Whatactuallyhappened: Whatwouldhavehappened:
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Wewanttocomparewhatwouldhavehappenedunder
noexposure(“treatment”)towhatactuallyhappenedtosomeonewhowasexposed(“treated”)
• E(Y1–Y0)
– Violenceexposure
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Wewanttocomparewhatwouldhavehappenedunder
noexposure(“treatment”)towhatactuallyhappenedtosomeonewhowasexposed(“treated”)
• E(Y1–Y0)
– Violenceexposure
Exposed Unexposed
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Wewanttocomparewhatwouldhavehappenedunder
noexposure(“treatment”)towhatactuallyhappenedtosomeonewhowasexposed(“treated”)
• E(Y1–Y0)
– Violenceexposure
Exposedalot UnexposedExposedali_le
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Wewanttocomparewhatwouldhavehappenedunder
noexposure(“treatment”)towhatactuallyhappenedtosomeonewhowasexposed(“treated”)
• E(Y1–Y0)
– Violenceexposure
Exposedalot UnexposedExposedali_le
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Weonlygettoobservewhatactuallyhappened
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Weonlygettoobservewhatactuallyhappened
• e.g.,exposedtoaspecificlevelofviolence
Exposedalot
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Weonlygettoobservewhatactuallyhappened
• e.g.,exposedtoaspecificlevelofviolence
– Butmaybedifferentfrominmanyways…
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Weonlygettoobservewhatactuallyhappened
• e.g.,exposedtoaspecificlevelofviolence
– …whichmayinstead/alsoexplainwhytheydifferontheoutcome(e.g.,mentalhealth)
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CommunityViolenceExposure&CausalInference
• PotenDaloutcomes(Ya)– Weonlygettoobservewhatactuallyhappened
• e.g.,exposedtoaspecificlevelofviolence
– Weonlyknowwhatanindividual’smentalhealthlookslikeunderwhichmaybethesameordifferentunder
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CommunityViolenceExposure&CausalInference
• RandomizaDon
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CommunityViolenceExposure&CausalInference
• RandomizaDon– Everyoneisassignedalevelofviolenceexposure
randomly
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CommunityViolenceExposure&CausalInference
• RandomizaDon– Everyoneisassignedalevelofviolenceexposure
randomly– Equalchanceofexposure,regardlessofallother
characterisDcs(e.g.,race,age,neighborhood)
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CommunityViolenceExposure&CausalInference
• RandomizaDon– Everyoneisassignedalevelofviolenceexposure
randomly– Equalchanceofexposure,regardlessofallother
characterisDcs(e.g.,race,age,neighborhood)– Onaverage,everyoneidenDcalexceptexposure
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CommunityViolenceExposure&CausalInference
• RandomizaDon– Notfeasible/ethical
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CommunityViolenceExposure&CausalInference
• HowweusuallytrytoapproximatecausaleffectsinobservaDonalstudies
– Longitudinaldata• Measure“baseline”levelsofoutcomes
– E.g.,aggression
• Adjustforcovariatesinregression– E.g.,baselineaggression,SES,age,neighborhood
• Problem:ExposureandoutcomevaryoverDme– AdjusDngforbaselineaggressionmay“adjustaway”true
effectofviolenceexposurealongthecausalpathway
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CommunityViolenceExposure&CausalInference
Y1!A2!Y2Aggression AggressionViolence
Exposure
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CommunityViolenceExposure&CausalInference
Y1!A2!Y2Aggression AggressionViolence
Exposure
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CommunityViolenceExposure&CausalInference
Y1!A2!Y2Aggression AggressionViolence
Exposure
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MarginalStructuralModels(MSM)
• SimulatetheactualandpotenDaloutcomes(counterfactural)usingobservaDonaldata
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MarginalStructuralModels(MSM)
• Suppose:
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MarginalStructuralModels(MSM)
• Suppose:– kidsalwaysnonaggressive(exposureirrelevant)
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MarginalStructuralModels(MSM)
• Suppose:– kidsalwaysnonaggressive(exposureirrelevant)
– kidshave25%chanceofbeingnonaggressiveifexposedtoviolence,&50%ifunexposed
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MarginalStructuralModels(MSM)
• Suppose:– kidsalwaysnonaggressive(exposureirrelevant)
– kidshave25%chanceofbeingnonaggressiveifexposedtoviolence,&50%ifunexposed
– (binaryexposurefornow,forsimplicity)
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MarginalStructuralModels(MSM)
• SupposepopulaDonlookslike:
Exposed:
Unexposed:
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MarginalStructuralModels(MSM)
• SupposepopulaDonlookslike:
Exposed:
Unexposed:
• Exposureisclearlyharmful
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MarginalStructuralModels(MSM)
• SupposepopulaDonlookslike:
Exposed:
Unexposed:
• Exposureisclearlyharmful– IfALLwereexposed,(6/10)0.25=15%wouldbenonaggressive
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MarginalStructuralModels(MSM)
• SupposepopulaDonlookslike:
Exposed:
Unexposed:
• Exposureisclearlyharmful– IfALLwereexposed,(6/10)0.25=15%wouldbenonaggressive
– IfNONEwereexposed,(6/10)0.5=30%wouldbenonaggressive
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MarginalStructuralModels(MSM)
• PopulaDon:
Exposed:
Unexposed:
• However,inourobserveddata,4/5(.25)=20%ofexposed&2/5(.5)=20%ofunexposedbecomenonaggressive
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MarginalStructuralModels(MSM)
• PopulaDon:
Exposed:
Unexposed:
• However,inourobserveddata,4/5(.25)=20%ofexposed&2/5(.5)=20%ofunexposedbecomenonaggressive
– Lookslikeexposurehasnoeffect!
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MarginalStructuralModels(MSM)
• PopulaDon:
Exposed:
Unexposed:
pr(exposed|=4/6
pr(exposed|=1/4
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pr(exposed|=4/6
pr(exposed|=1/4
MarginalStructuralModels(MSM)
• PopulaDon:
Exposed:
Unexposed:pr(unexposed|=2/6
pr(unexposed|=3/4
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pr(exposed|=4/6
pr(exposed|=1/4
MarginalStructuralModels(MSM)
• PopulaDon:
Exposed:
Unexposed:
Nowwecancreateapseudo-popula3onbyweighDngeachkidbytheinverseprobabilityofreceivingtheirobservedtreatment(Robinsetal.,2000)
pr(unexposed|=2/6
pr(unexposed|=3/4
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MarginalStructuralModels(MSM)
• Pseudo-populaDon:
Exposed*:
Unexposed*:
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MarginalStructuralModels(MSM)
• Pseudo-populaDon:
Exposed*:
Unexposed*:
Downweightthosewhoareover-representedinpopulaDon,&upweighttheunder-represented
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MarginalStructuralModels(MSM)
• Pseudo-populaDon:
Exposed*:
Unexposed*:
Inthepseudo-populaDon:– 6/10(.25)=15%ofexposedwerenonaggressive
– 6/10(.5)=30%ofunexposedwerenonaggressive
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MarginalStructuralModels(MSM)
• Pseudo-populaDon:
Exposed*:
Unexposed*:
• Matchesthecounterfactualnumbers!
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MarginalStructuralModels(MSM)
• Pseudo-populaDon:
Exposed*:
Unexposed*:
• Matchesthecounterfactualnumbers!• SameasrandomizaDon
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MarginalStructuralModels(MSM)
• WeighDng=creaDngapseudo-populaDonwhere:
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MarginalStructuralModels(MSM)
• WeighDng=creaDngapseudo-populaDonwhere:– ThecovariatedistribuDonisthesameasinthe
populaDon
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MarginalStructuralModels(MSM)
• WeighDng=creaDngapseudo-populaDonwhere:– ThecovariatedistribuDonisthesameasinthe
populaDon
– ThereisnoassociaDonbetweentreatment&covariates
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MarginalStructuralModels(MSM)
• WeighDng=creaDngapseudo-populaDonwhere:– ThecovariatedistribuDonisthesameasinthe
populaDon
– ThereisnoassociaDonbetweentreatment&covariates
Y1!A2!Y2Aggression AggressionViolence
Exposure
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MarginalStructuralModels(MSM)
• WeighDng=creaDngapseudo-populaDonwhere:– ThecovariatedistribuDonisthesameasinthe
populaDon
– ThereisnoassociaDonbetweentreatment&covariates
A2!Y2AggressionViolence
Exposure
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MarginalStructuralModels(MSM)
• WeighDng=creaDngapseudo-populaDonwhere:– ThecovariatedistribuDonisthesameasinthe
populaDon
– ThereisnoassociaDonbetweentreatment&covariates
– Thus,noconfounding• CausaleffectscanbeesDmatedwithoutaddiDonaladjustment
A2!Y2AggressionViolence
Exposure
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MarginalStructuralModels(MSM)
• Time-varyingextensionofpropensityscoreweighDng
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HowtoApplyMSM
1. Fitpropensityscoremodelofprobabilityofexposure
2. Doaweightedregression(usingtheIPWs)
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Assump3ons
1. Consistency:Y=YawhenA=a– Weobservetheoutcomethatagivenlevelofexposure
causeswhenweobservethatexposure
2. PosiDvity:pr(A=a|L=l)>0– TheremustexistaposiDveprobabilityofallexposure
levelsforallstrataofcovariates
– Noonemayhave0probabilityofexposure
3. Ignorability:Nounmeasuredconfounding
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Results
• DoescommunityviolenceexposuredifferenDallyaffectyouths’mentalhealth?
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Results
• DoescommunityviolenceexposuredifferenDallyaffectyouths’mentalhealth?
– DesensiDzaDonhypothesis:• QuadraDceffectoninternalizingsymptoms
• Lineareffectonexternalizingsymptoms
CVE
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Results
• Data– ProjectonHumanDevelopmentinChicago
Neighborhoods(PHDCN)
(Earls,Brooks-Gunn,Raudenbush,&Sampson,1994-2002)
– LongitudinalCohortStudy– Youth&primarycaregiver– StraDfiedprobabilitysample:N=4,149– Waves2&3– CohortagesatWave2:6,9,12,15,18– RepresentaDvesample
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Results
• Measures– Baselinedemographiccovariates
• Age,sex,race,income,SES
– Communityviolenceexposure(CVE)• MyExposuretoViolence• PastyearCVEfrequencyon20items
• Witnessing&vicDmizaDon
– Mentalhealth• CBCLInternalizing&Externalizing
– ControlledforpriorlevelsinGEEmodels
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Results
• GEE&MSM• CVE2!agg2&CVE3!agg3• Baselinecovariates&prioraggression
• CondiDonaldensi>esinsteadofprobabiliDesforconDnuousexposure(CVE)
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Results:GEEInternalizing
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Results:GEEExternalizing
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Results:MSM
0 5 10 15
52.0
52.5
53.0
53.5
54.0
54.5
55.0
55.5
Violence Exposure
Inte
rnal
izin
g Sc
ore
0 5 10 15
6.5
7.0
7.5
8.0
8.5
9.0
Violence Exposure
Exte
rnal
izin
g Sc
ore
Internalizing Externalizing
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Conclusions
• DesensiDzaDoneffectofCVE– PathologicadaptaDon?
• Similar,butslightlyweakereffectsusingMSM– SomeDmeseffectdisappears,orreversesdirecDon
• MoreaccuratecausaleffectofCVE• IntervenDonimplicaDons
– Everyoneexposedvseveryoneunexposed
• (InteracDonswerens)
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Limita3ons
• ModelspecificaDon– Moreflexibility(splines)probablyneeded
• CVEmeasurement– RetrospecDvereport– Ordinalcoding– Timebetweenassessments
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Limita3ons
• MSMassumpDons– Likelyunmeasuredconfounding
• E.g.,parenDng,schoola_endance,etc.
– PosiDvity• MaybeyouthwithzeroprobabilityofCVE
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MSMTips
• SpecifyquesDonpreciselytooperaDonalize&isolatecausaleffectofinterest
– RCTframework• E.g.:
– Whoexactlyarethesubjects?– Whatexactlyisthetreatment?
– Forexactlyhowlongaretheytreated?
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MSMTips
• Avoidunmeasuredconfounding– Measureallpossibleconfounders
• FancystaDsDcscannotfixbaddesigns
– SensiDvityanalysis• VanderWeele(2010)BiasformulasforsensiDvityanalysisfordirectand
indirecteffects,Epidemiology,21,540-551• Brumbacketal(2004)SensiDvityanalysesforunmeasuredconfounding
assumingamarginalstructuralmodelforrepeatedmeasures,Sta>s>csinMedicine,23(5),749-767
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MSMTips
• Usestabilizedweights– InverseprobabiliDesbecomeunwieldy
– Incorporatebaselinecovariatestostabilize– Robinsetal(2000)Marginalstructuralmodelsandcausalinferenceinepidemiology,
Epidemiology,11,550-560
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MSMExtensions
• AlthoughMSMsovenuseIPW,otherapproachestoesDmateMSMparameters:
– Regression-basedg-computaDon– DoublyrobustesDmaDngequaDons– Targetedmaximumlikelihood(TMLE)
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MSMExtensions
• MLM,growthcurvemodeling,SEM,etc.– SimplyapplyIPWweights
• MediaDon(e.g.,Coffman&Zhong,2012;VanderWeele)
• AddiDonalDmepoints• CompoundedeffectsoverDme
– E.g.,effectsofCVE2&CVE3onagg3
• Effectofremovingvsaddingexposure• IncrementalintervenDons(Kennedy,underreview)
– MorerealisDcintervenDonimplicaDons
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Applica3ons
• Typicallymedicine&epidemiology– E.g.,HIVtreatment
• Expandtopsychology&socialsciences– Time-dependentconfounding&reciprocaleffects– E.g.:
• Mentalhealthtreatment
• Bullying
• ADHDsDmulantmedicaDons• RCTswithnoncompliance
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ConcludingThoughts
• UseMSMs!• R,SAS,&Stata
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References&Resources
• Conceptual– Robins&Hernánbookdrav:
h_ps://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
– Robinsetal(2000)–Epidemiology
– Robins&Hernán(2009)–Chapter1inLongitudinalDataAnalysis
– Faries&Kadziolachapter:AnalysisoflongitudinalobservaDonaldatausingmarginalstructuralmodels
– VanderWeele(2009)–Epidemiology• MediaDon
– Kennedy(underreview)NonparametriccausaleffectsbasedonincrementalpropensityscoreintervenDons
h_ps://arxiv.org/abs/1704.00211
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References&Resources
• Applied– Bacak&Kennedy(2015)–J.ofMarriageandFamily
• Marriage&recidivism– Hernánetal(2002)–Sta>s>csinMedicine
• HIVtreatmenteffecDveness– Pateletal(2008)–ClinicalInfec>ousDiseases
• PediatricHIVtreatmenteffecDveness– VanderWeeleetal(2011)–JCCP
• Loneliness&depression– VanderWeeleetal(2016)–SocPsychiatry&Psychiatr
Epidem• Religion&mentalhealth
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References&Resources• SoUware
– R• Bacak&Kennedy(2015)–J.ofMarriageandFamily• Coffman&Zhong(2012)–PsychologicalMethods• Moerkerkeetal(2015)–PsychologicalMethods
– MediaDon
– SAS• Faries&Kadziolachapter:Analysisoflongitudinal
observaDonaldatausingmarginalstructuralmodels• Crowsonetal(2013)Thebasicsofpropensityscoringand
marginalstructuralmodels
– SAS,Stata,&R• Robins&Hernánbookdrav
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Conferences&Workshops• PennCausalInference&BigDataSummerInsDtute
July24-27,2017–EdwardKennedyh_p://www.med.upenn.edu/cbd/
• CausalInferenceMethodsforPCORusingObservaDonalData(CIMPOD)–NIHh_p://cimpod2017.org/
• AtlanDcCausalInferenceConferenceMay2018–CarnegieMellonUniversityh_p://causal.unc.edu/acic2017/
• StaDsDcalHorizons–CausalMediaDonAnalysisOctober13-14,2017–TylerVanderweeleh_ps://staDsDcalhorizons.com/seminars/public-seminars
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Acknowledgments• RosarioCeballo&JimCranford
• MarshallJoffe&DylanSmall
• BrookeMolina
• AndreaHoward
• KunjalPatel
Currentresearchsupport:DA039881;AA011873;HD083404(PI:BrookeS.G.Molina)