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: 10269293
• 负责人: Elizabeth A. Stuart
• 金额: $ 45.05万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10269293
• 关键词: Adoption; Area; Attention Deficit Disorder; Bayesian Method; Bayesian learning; Behavior Disorders; Behavioral; Big Data; Categories; Characteristics; Childhood; Data; Data Set; Data Sources; Diagnosis; Disease; Effectiveness; Electronic Health Record; Exhibits; Face; Genetic; Goals; Gold; Health system; Healthcare; Heterogeneity; Individual; Injections; Intervention; Learning; Major Depressive Disorder; Measurement; Measures; Medical; Mental Depression; Mental Health; Meta-Analysis; Methods; Modeling; National Institute of Mental Health; Outcome; Palmitates; Patient Rights; Patients; Performance; Pharmaceutical Preparations; Population; Prevention; Randomized; Randomized Controlled Trials; Research; Research Design; Research Personnel; Resources; Risperidone; Sampling Studies; Schizophrenia; Strategic Planning; System; Testing; Time; Translations; Treatment outcome; Universities; Work; care outcomes; clinical care; clinical decision-making; duloxetine; experimental study; health care quality; improved; interest; machine learning method; multiple data sources; personalized intervention; point of care; prevent; preventive intervention; randomized trial; treatment effect; treatment response

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.

项目概要 确定“什么对谁有效”是各种疾病预防和治疗的关键目标 领域，包括心理健康。 我们有可能将稀缺资源分配给那些最受益的人， 并减少个人在找到一种治疗方法之前尝试多种治疗方法的“流失” 确定对他们有效的效应调节因素——与治疗规模相关的因素。 效果——对于提供治疗和预防干预措施至关重要，但这样做 使用标准研究设计（黄金标准）非常困难。 估计平均效果通常不足以检测大规模非调节。 实验研究可能提供另一种方法来检验效果调节，但可能会受到影响 需要新的方法来最好地利用可用的数据来学习如何 这项工作将综合、扩展和应用心理健康治疗方法。 当有多项研究可用时，确定效应调节因素，特别关注 这些方法将广泛应用于心理健康研究，并将在中进行说明。 使用来自 11 个国家的数据估计精神分裂症药物治疗效果的示例 来自杜克大学健康系统的随机对照试验和非实验数据 电子健康记录的工作将： 1）扩展场景的审核方法。 多个随机实验，2) 开发使用组合数据集中的数据的方法 通过实验和非实验设计来确定效果调节，以及 3) 通过开发充分利用的方法，向心理健康研究人员传播这些方法。 实验和非实验数据的优势这项工作有潜力推动 个性化的心理健康，从而改善我们预防和治疗心理健康的方式 人口中的挑战。