Using Symptom Network Models to Translate Theory to Clinical Applications

使用症状网络模型将理论转化为临床应用

• 批准号: 10491738
• 负责人: William E Conlin
• 金额: $ 3.8万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2024-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10491738
• 关键词: Adopted; Alcohols; Classification; Clinical; Cluster Analysis; Complex; Conduct Disorder; Consultations; Data; Data Analyses; Development; Developmental Process; Diagnosis; Diagnostic; Diagnostic and Statistical Manual of Mental Disorders; Disease; Environment; Epidemiology; Etiology; Factor Analysis; Funding; Goals; Grant; Heavy Drinking; Heterogeneity; Human; Individual; Instruction; Intervention; Knowledge; Lead; Manuals; Mental disorders; Mentors; Mentorship; Missouri; Modeling; Modernization; National Institute on Alcohol Abuse and Alcoholism; Outcome; Pattern; Play; Process; Recording of previous events; Recovery; Recurrence; Research; Research Design; Research Methodology; Research Personnel; Risk Factors; Role; Sampling; Science; Statistical Methods; Structure; Subgroup; Substance Withdrawal Syndrome; Surveys; Symptoms; Syndrome; Techniques; Theoretical model; Training; Training Programs; Translating; Treatment outcome; United States National Institutes of Health; Universities; Wood material; Work; Writing; addiction; alcohol abuse therapy; alcohol use disorder; allostasis; career; clinical application; clinical practice; clinical predictors; design; diagnostic criteria; diagnostic platform; experience; improved; insight; member; network models; novel; open data; pre-clinical; pre-clinical research; precision medicine; predict clinical outcome; psychologic; skills; statistics; symposium; theories

PROJECT SUMMARY/ABSTRACT Broad/Long Term Objectives: The proposed research has two broad goals: to improve diagnostic conceptualization of AUD by adopting a symptom-focused approach that is more consistent with contemporary theoretical models of addiction (e.g., allostasis); and to ascertain the extent to which this approach can be translated into clinical applications. Specific Aims: The aims of the proposed project are to: characterize how individual AUD symptoms uniquely predict the onset, persistence, and recurrence (course) of other symptoms; resolve diagnostic heterogeneity and improve classification by examining symptom structure in a priori and empirically derived subgroups; and analyze the extent to which different symptoms and symptom subgroups predict clinical outcomes across different forms of treatment. Research Design and Method: The project will consist of secondary data analysis using both waves of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), the COMBINE study, and Project MATCH. In NESARC, symptom network modelling (SNM) will be used to identify key symptoms predicting the course of the other symptoms and identify core features of symptom subgroups. The project will also compare a priori subgroups based on etiologic (e.g., Conduct Disorder, heavy drinking patterns) risk factors and subgroups of symptom profiles empirically derived via cluster analysis. In MATCH and COMBINE, general mixed models and group factor analysis will be applied analyze the interaction between symptom profile, treatment condition, and treatment outcomes. Significance: This project will advance the understanding of how AUD diagnostic criteria reflect the endogenous processes proposed by modern addiction theories, help resolve diagnostic heterogeneity, and improve diagnostic validity. Additionally, the results of the project will allow for more effective tailoring and implementation of focused assessment and identification of potential targets for treatment. Training Plan and Environment: The training plan is designed to provide the applicant with quantitative, substantive, and practical training to facilitate a successful career as an independent investigator. The applicant will receive training in advanced multivariate statistics, application of theoretical models of addiction to clinical outcomes, open sciences practices, and general scientific writing. Training will take place at the University of Missouri’s Department of Psychological Sciences, which has an outstanding addiction training program funded by an NIAAA training grant (T32 AA013526; PI: Kenneth Sher). The mentoring team consists of experts in quantitative (Dr. Steinley, Dr. Wood) and substantive (Dr. Sher, Dr. Witkiewitz) research on AUD. Members of the team have a long collegial history, providing a synergistic training experience for the applicant.

项目概要/摘要 广泛/长期目标：拟议的研究有两个广泛的目标：改善诊断 通过采用更符合当代的以症状为中心的方法来对 AUD 进行概念化 成瘾的理论模型（例如，动态平衡）；并确定这种方法可以在多大程度上得到实施 转化为临床应用。 具体目标：拟议项目的目标是：描述个体 AUD 症状的独特特征 预测其他症状的发生、持续和复发（病程）；解决诊断异质性和 通过检查先验和经验得出的亚组中的症状结构来改进分类；并分析 不同症状和症状亚组预测不同形式临床结果的程度 的治疗。 研究设计和方法：该项目将包括使用两次波的二次数据分析 全国酒精及相关病症流行病学调查 (NESARC)、COMBINE 研究和项目 匹配。在 NESARC 中，症状网络模型 (SNM) 将用于识别预测症状的关键症状 其他症状的病程并确定症状亚组的核心特征。该项目还将比较 基于病因（例如行为障碍、酗酒模式）风险因素的先验亚组和 通过聚类分析凭经验得出症状特征的亚组。在MATCH和COMBINE中，一般混合 将应用模型和组因素分析来分析症状特征、治疗之间的相互作用 病情和治疗结果。 意义：该项目将增进对 AUD 诊断标准如何反映内生性的理解 现代成瘾理论提出的过程有助于解决诊断异质性，并改善 诊断有效性。此外，该项目的结果将有助于更有效的调整和实施 重点评估和确定潜在治疗目标。 培训计划和环境：培训计划旨在为申请人提供定量、 实质性和实用的培训，以促进作为独立调查员的成功职业生涯。申请人 将接受高级多元统计、成瘾理论模型在临床中的应用方面的培训 结果、开放科学实践和一般科学写作。培训将在大学进行 密苏里州心理科学系资助了一项杰出的成瘾训练项目 由 NIAAA 培训补助金（T32 AA013526；PI：Kenneth Sher）提供。导师团队由各领域专家组成 AUD 的定量（Steinley 博士、Wood 博士）和实质性（Sher 博士、Witkiewitz 博士）研究。成员 该团队拥有悠久的学院历史，为申请人提供协同培训经验。