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1、AStataPluginforEstimatingGroup-BasedTrajectoryModelsBobbyL.JonesUniversityofPittsburghMedicalCenterDanielS.NaginCarnegieMellonUniversityMay21,2012ThisworkwasgenerouslysupportedbyNationalScienceFoundationGrantsSES-102459andSES-0647576AbstractGroup-basedtrajectorymodelsareusedtoi
2、nvestigatepopulationdifferencesinthedevelopmentalcoursesofbehaviorsoroutcomes.ThisarticledemonstratesanewStatacommand,traj,forfittingtolongitudinaldatafinite(discrete)mixturemodelsdesignedtoidentifyclustersofindividualsfollowingsimilarprogressionsofsomebehaviororoutcomeoverageo
3、rtime.Censorednormal,Poisson,zero-inflatedPoisson,andBernoullidistributionsaresupported.Applicationstopsychometricscaledata,countdata,andadichotomousprevalencemeasureareillustrated.IntroductionAdevelopmentaltrajectorymeasuresthecourseofanoutcomeoverageortime.Thestudyofdevelopme
4、ntaltrajectoriesisacentralthemeofdevelopmentalandabnormalpsychologyandpsychiatry,oflifecoursestudiesinsociologyandcriminology,ofphysicalandbiologicaloutcomesinmedicineandgerontology.Awidevarietyofstatisticalmethodsareusedtostudythesephenomena.ThisarticledemonstratesaStataplugin
5、forestimatinggroup-basedtrajectorymodels.TheStataprogramwedemonstrateadaptsawell-establishedSAS-basedprocedureforestimatinggroup-basedtrajectorymodel(Jones,Nagin,andRoeder,2001;JonesandNagin,2007)totheStataplatform.Group-basedtrajectorymodelingisaspecializedformoffinitemixturem
6、odeling.Themethodisdesignedidentifygroupsofindividualsfollowingsimilarlydevelopmentaltrajectories.Forarecentreviewofapplicationsofgroup-basedtrajectorymodelingseeNaginandOdgers(2010)andforanextendeddiscussionofthemethod,includingtechnicaldetails,seeNagin(2005).ABriefOverviewofG
7、roup-BasedTrajectoryModelingUsingfinitemixturesofsuitablydefinedprobabilitydistributions,thegroup-basedapproachformodelingdevelopmentaltrajectoriesisintendedtoprovideaflexibleandeasilyappliedmethodforidentifyingdistinctiveclustersofindividualtrajectorieswithinthepopulationandfo
8、rprofilingthecharacteristicsofindividualswithintheclus