grant
Statistical methods for predicting individualized intervention effects with clustered and longitudinal data
Organization UNIVERSITY OF CALIFORNIA AT DAVISLocation DAVIS, UNITED STATESPosted 1 Apr 2026Deadline 31 Mar 2028
NIHUS FederalResearch GrantFY20260-11 years oldASDAddressAffectAgeAreaAutismAutistic DisorderBehaviorBehavioralBig DataBig Data AnalyticsBig Data MethodsBig Data ToolsBigDataBiological MarkersBrainBrain Nervous SystemBrain regionCaliforniaCausalityCharacteristicsChildChild YouthChildren (0-21)CommunitiesComplexComputer softwareDataData SetDevelopmentDiagnosisDiseaseDisorderEarly Infantile AutismEncephalonEnvironmental ExposureEpidemiologyEtiologyExhibitsExposure toFaceFamilyFundingGestationGoalsHealth Care ProvidersHealth PersonnelHigh-Risk PregnancyHumanImmune Cell ActivationIndividualInfantile AutismInfectionInterventionInvestigatorsKanner's SyndromeKnowledgeLinkLongitudinal StudiesLongitudinal SurveysMachine LearningMeasurementMedicalMedical ResearchMental HealthMental HygieneMental disordersMental health disordersMethodologyMethodsModelingModern ManModernizationMothersNIMHNational Institute of Mental HealthNational Institutes of HealthNeurodevelopmental DisorderNeurological Development DisorderOutcomeOutcome MeasurePatient outcomePatient-Centered OutcomesPatient-Focused OutcomesPatientsPerformancePredictive FactorPredispositionPregnancyPreventative measurePreventive measureProbabilistic ModelsProbability ModelsPsychiatric DiseasePsychiatric DisorderPsychological HealthResearchResearch PersonnelResearchersRight to TreatmentsRiskRisk FactorsSchizophreniaSchizophrenic DisordersServicesSoftwareStatistical MethodsStatistical ModelsStructureSubgroupSusceptibilityTechniquesTestingTimeUnited States National Institutes of HealthUniversitiesWorkagesanalytical toolanimal dataautism spectral disorderautism spectrum disorderautistic spectrum disorderbio-markersbiologic markerbiomarkerbiomarker identificationcausationcomplex datacustomized therapycustomized treatmentdata frameworkdementia praecoxdesigndesigningdevelopmentaldisease causationepidemiologicepidemiologicalexposed in uteroexposure to environmental agentsexposure to environmental factorsexposure to environmental stimuliexposure to environmental substancesfacesfacialfetal exposurehealth care personnelhealth care workerhealth providerhealth staffhealth workershealth workforcehealthcare employeeshealthcare staffhealthcare workforcehigh dimensionalityhigh riskhuman dataidentification of biomarkersidentification of new biomarkersimmune activationimproved outcomein utero exposureindividual patientindividualized medicineindividualized patient treatmentindividualized predictionsindividualized therapeutic strategyindividualized therapyindividualized treatmentinnovateinnovationinnovativeinsightinterestintervention effectintra-uterine environmental exposureintrauterine environmental exposurekidslong-term studylongitudinal data setlongitudinal datasetlongitudinal outcome studieslongitudinal research studymachine based learningmachine statistical learningmarker identificationmeasurable outcomemedical care providersmedical personnelmental illnessneurodevelopmental diseasenoveloffspringoutcome measurementpatient oriented outcomespatient specific therapiespatient specific treatmentpatient subclasspatient subclusterpatient subgroupspatient subpopulationspatient subsetspatient subtypespersonalized health interventionpersonalized interventionpersonalized predictionsphenotypic dataprecision interventionsprecision medicineprecision-based medicineprenatal exposureprenatally exposedprogramspsychiatric illnesspsychological disorderresilienceresilientresponseschizophrenicside effectsimulationstatistic methodsstatistical and machine learningstatistical linear mixed modelsstatistical linear modelstailored medical treatmenttailored therapytailored treatmenttherapy optimizationtooltreatment choicetreatment effecttreatment optimizationtreatment providerunique treatmentuser-friendlyyoungster
Description preview
PROJECT ABSTRACT
Identifying individuals likely to respond to a specific intervention is a critical challenge in medical research. In
the age of big data, where vast amounts of information are accessible, the potential for personalized
interventions based on individual characteristics has become increasingly feasible. However, advancement
comes…
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