Combining data sources to identify effect moderation for personalized mental health treatment
结合数据源来确定个性化心理健康治疗的效果调节
基本信息
- 批准号:10269293
- 负责人:
- 金额:$ 45.05万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-19 至 2025-05-31
- 项目状态:未结题
- 来源:
- 关键词:AdoptionAreaAttention Deficit DisorderBayesian MethodBayesian learningBehavior DisordersBehavioralBig DataCategoriesCharacteristicsChildhoodDataData SetData SourcesDiagnosisDiseaseEffectivenessElectronic Health RecordExhibitsFaceGeneticGoalsGoldHealth systemHealthcareHeterogeneityIndividualInjectionsInterventionLearningMajor Depressive DisorderMeasurementMeasuresMedicalMental DepressionMental HealthMeta-AnalysisMethodsModelingNational Institute of Mental HealthOutcomePalmitatesPatient RightsPatientsPerformancePharmaceutical PreparationsPopulationPreventionRandomizedRandomized Controlled TrialsResearchResearch DesignResearch PersonnelResourcesRisperidoneSampling StudiesSchizophreniaStrategic PlanningSystemTestingTimeTranslationsTreatment outcomeUniversitiesWorkcare outcomesclinical careclinical decision-makingduloxetineexperimental studyhealth care qualityimprovedinterestmachine learning methodmultiple data sourcespersonalized interventionpoint of carepreventpreventive interventionrandomized trialtreatment effecttreatment response
项目摘要
Project Summary
Determining “what works for whom” is a key goal in prevention and treatment across a variety of
areas, including mental health. By understanding which individuals benefit most from which
treatments we have the possibility of directing scarce resources to those who will most benefit,
and of reducing the “churn” of individuals attempting multiple treatments before finding the one
that works for them. Identifying effect moderators—factors that relate to the size of treatment
effects--is crucial for delivery of treatment and prevention interventions, but doing so is
incredibly difficult using standard study designs. Randomized trials, the gold standard for
estimating average effects, are typically under-powered to detect moderation. Large-scale non-
experimental studies may provide another way to examine effect moderation, but can suffer
from confounding. New methods are needed to best harness the data available to learn how to
personalize mental health treatments. This work will synthesize, extend, and apply methods for
identifying effect moderators when multiple studies are available, with a particular focus on the
complexities in mental health research. The methods will apply broadly and will be illustrated in
an example estimating the effects of medication treatment for schizophrenia, using data from 11
randomized controlled trials and non-experimental data from the Duke University Health System
electronic health record. The work will: 1) Extend moderation methods for scenarios with
multiple randomized experiments, 2) Develop methods for using data from combined datasets
with both experimental and non-experimental designs to identify effect moderation, and 3)
Disseminate the methods to mental health researchers. By developing methods to take full
advantage of both experimental and non-experimental data this work has the potential to move
towards personalized mental health, thus improving how we prevent and treat mental health
challenges in the population.
项目摘要
确定“什么对谁有效”是各种疾病预防和治疗的一个关键目标。
包括心理健康。通过了解哪些人从中受益最多
我们有可能将稀缺的资源用于那些最受益的人,
以及减少在找到一种治疗方法之前尝试多种治疗方法的个人的“流失”
为他们工作。识别效应调节因子-与治疗规模相关的因素
对提供治疗和预防干预措施至关重要,但这样做
使用标准的研究设计是非常困难的。随机试验,
估计平均效应通常不足以检测适度。大型非
实验研究可能会提供另一种方法来检查效果适度,但可能会遭受
从混淆。需要新的方法来最好地利用可用的数据,以了解如何
个性化的心理健康治疗。这项工作将综合,扩展和应用方法,
当有多项研究可用时,确定效应调节因子,特别关注
心理健康研究的复杂性。这些方法将广泛适用,并将在
一个例子估计药物治疗精神分裂症的效果,使用11
来自杜克大学卫生系统的随机对照试验和非实验数据
电子健康记录这项工作将:1)扩展缓和方法的情况下,
多个随机实验,2)开发使用来自组合数据集的数据的方法
用实验和非实验设计来识别效果调节,以及3)
向心理健康研究人员传播这些方法。通过开发方法,
实验和非实验数据的优势,这项工作有可能移动
个性化的心理健康,从而改善我们如何预防和治疗心理健康,
人口中的挑战。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Elizabeth A. Stuart其他文献
The Lancet Psychiatry Commission: transforming mental health implementation research.
柳叶刀精神病学委员会:转变心理健康实施研究。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:64.3
- 作者:
E. Mcginty;Margarita Alegria;R. Beidas;Jeffrey Braithwaite;Lola Kola;Douglas L Leslie;Nathalie Moise;Bernardo Mueller;H. A. Pincus;Rahul Shidhaye;Kosali Simon;Sara J Singer;Elizabeth A. Stuart;Matthew D Eisenberg - 通讯作者:
Matthew D Eisenberg
The association between cortisol and neighborhood disadvantage in a U.S. population-based sample of adolescents
- DOI:
10.1016/j.healthplace.2013.11.001 - 发表时间:
2014-01-01 - 期刊:
- 影响因子:
- 作者:
Kara E. Rudolph;Wand Gary S.;Elizabeth A. Stuart;Thomas A. Glass;Andrea H. Marques;Roman Duncko;Kathleen R. Merikangas - 通讯作者:
Kathleen R. Merikangas
Assets and depression in U.S. adults during the COVID-19 pandemic: a systematic review
- DOI:
10.1007/s00127-023-02565-2 - 发表时间:
2023-10-15 - 期刊:
- 影响因子:3.500
- 作者:
Catherine K. Ettman;Maya Subramanian;Alice Y. Fan;Gaelen P. Adam;Salma M. Abdalla;Sandro Galea;Elizabeth A. Stuart - 通讯作者:
Elizabeth A. Stuart
Efectos de la Exposición de los Adolescentes a la Violencia en la Comunidad: El Proyecto MORE
社区暴力对青少年的影响:El Proyecto 更多
- DOI:
10.5093/in2011v20n2a2 - 发表时间:
2011 - 期刊:
- 影响因子:4.8
- 作者:
Michele Cooley;Tanya J. Quille;Rob Griffin;Elizabeth A. Stuart;Catherine P. Bradshaw;D. Furr - 通讯作者:
D. Furr
Using Potential Outcomes to Understand Causal Mediation Analysis: Comment on Maxwell, Cole, and Mitchell (2011)
使用潜在结果来理解因果中介分析:评论麦克斯韦、科尔和米切尔 (2011)
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
K. Imai;Booil Jo;Elizabeth A. Stuart - 通讯作者:
Elizabeth A. Stuart
Elizabeth A. Stuart的其他文献
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{{ truncateString('Elizabeth A. Stuart', 18)}}的其他基金
Combining data sources to identify effect moderation for personalized mental health treatment
结合数据源来确定个性化心理健康治疗的效果调节
- 批准号:
10629398 - 财政年份:2021
- 资助金额:
$ 45.05万 - 项目类别:
Combining data sources to identify effect moderation for personalized mental health treatment
结合数据源来确定个性化心理健康治疗的效果调节
- 批准号:
10471956 - 财政年份:2021
- 资助金额:
$ 45.05万 - 项目类别:
Data integration for causal inference in behavioral health
行为健康因果推理的数据集成
- 批准号:
10649426 - 财政年份:2020
- 资助金额:
$ 45.05万 - 项目类别:
Data integration for causal inference in behavioral health
行为健康因果推理的数据集成
- 批准号:
10393600 - 财政年份:2020
- 资助金额:
$ 45.05万 - 项目类别:
Data integration for causal inference in behavioral health
行为健康因果推理的数据集成
- 批准号:
10164866 - 财政年份:2020
- 资助金额:
$ 45.05万 - 项目类别:
Mental Health Services and Systems Training Program
心理健康服务和系统培训计划
- 批准号:
10624522 - 财政年份:2017
- 资助金额:
$ 45.05万 - 项目类别:
Using propensity scores for causal inference with covariate measurement error
使用倾向得分进行带有协变量测量误差的因果推断
- 批准号:
9102249 - 财政年份:2013
- 资助金额:
$ 45.05万 - 项目类别:
Using propensity scores for causal inference with covariate measurement error
使用倾向得分进行带有协变量测量误差的因果推断
- 批准号:
8576817 - 财政年份:2013
- 资助金额:
$ 45.05万 - 项目类别:
Using propensity scores for causal inference with covariate measurement error
使用倾向得分进行带有协变量测量误差的因果推断
- 批准号:
8690155 - 财政年份:2013
- 资助金额:
$ 45.05万 - 项目类别:
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