Data-Driven Discovery of Heterogeneous Treatment Effects of Statin Use on Dementia Risk
他汀类药物使用对痴呆风险的异质治疗效果的数据驱动发现
基本信息
- 批准号:10678219
- 负责人:
- 金额:$ 4.39万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-01 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:AffectAgeAgingAlgorithmsAlzheimer&aposs disease related dementiaAlzheimer&aposs disease riskAmericanAwardBehavioralBiologicalBiological MarkersCharacteristicsClinicalClinical TrialsCognitive agingConsensusDataData SetDementiaDiagnosisDiseaseEconomicsEffectivenessGeneticGoalsGrantHeterogeneityHyperlipidemiaImpaired cognitionIndividualInvestigationMachine LearningMentorshipMethodsMissionModificationNatureObservational StudyParticipantPharmaceutical PreparationsPoliciesPrevalencePreventionPrevention ResearchPrevention strategyProbabilityProspective cohortPsychometricsPublic HealthQualifyingRaceReproducibility of ResultsResearchResearch MethodologyResearch PersonnelResearch ProposalsResearch TrainingResourcesRisk ReductionSamplingSubgroupTestingTrainingTreesUnited Statesadvanced dementiaagedapolipoprotein E-4biobankcareercognitive functioncohortcomorbiditydementia riskdesigneffective therapyfollow-upforestimprovedinnovationinterestlipid metabolismmachine learning algorithmmachine learning methodnoveloptimal treatmentspatient subsetsprecision medicinepreventrecruitregression treessexskillssocialsociodemographicsstudy populationtreatment effect
项目摘要
PROJECT SUMMARY (ABSTRACT)
Alzheimer’s Disease and Related Dementias (ADRD) currently affects more than 4 million Americans and over
50 million individuals worldwide. The identification of prevention strategies for dementia is critical, particularly
due to the lack of effective treatments. In parallel, there is growing consensus that lipid metabolism is a major
contributor to ADRD and may be an important strategy for risk reduction and prevention. Antihyperlipidemic
agents (i.e., statins) are widely used, yet evidence on the relationship between antihyperlipidemic agents (i.e.,
statins) and ADRD has been largely inconclusive. One possible explanation for the mixed findings is
heterogeneity in study populations and their characteristics. For example, the effectiveness of statins is
evidenced to vary by age, ApoE4 status, and pre-existing disease status.34-36 Accordingly, there is a growing
need to identify the factors (i.e., effect modifiers) which influence heterogeneities in the effect of statins on
dementia. The objective of this study is to triangulate evidence on the identification and estimation of
heterogeneous treatment effects by using three causal machine learning methods, specifically the honest
causal forest/policy tree, doubly robust adaptive LASSO, and Bayesian Adaptive Regression Trees (BART), to
identify novel effect modifiers and optimal subgroups for the effect of statins on dementia. While traditional
parametric regression approaches are designed to test a priori hypotheses regarding effect modification, such
approaches are not suitable for yielding novel hypotheses. The causal machine learning methods described in
this proposal fill this gap; not only do such approaches help identify novel effect modifiers, but they can also
facilitate the subsequent identification of optimal treatment rules across those modifiers. In this study, I propose
to use a cohort of 307,719 individuals from the UK Biobank data who were at least 55 when they were initially
recruited from 2006 to 2010. The analytical sample will be large, allowing me to rigorously investigate
heterogeneous treatment effects across different subgroups. Specifically, in Aim 1, I propose to estimate the
real-world average treatment effect (ATE) of statins on ADRD across the entire sample. I will then, in Aim 2,
apply three causal machine learning algorithms to identify novel effect modifiers and corresponding optimal
subgroups for the effect of statin use on ADRD risk. Finally, in Aim 3, I will quantify the reduction in ADRD
cases that would result from implementing each of the optimal treatment rules generated under Aim 2 and
compare them to the reduction in ADRD cases observed under Aim 1. This F31 proposal application will
support my dissertation research, as well as my interest in gaining training in causal machine learning, as well
as substantive training in dementia, cognitive aging, and its psychometric methods. Under the guidance of my
mentorship team, I look forward to advancing dementia prevention research while also pursuing my goal of
becoming an independent investigator in research methods on cognitive aging.
项目总结(摘要)
项目成果
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