Classification of Substance Use Severity and Treatment Outcomes: NIDA CTN Studies

描述（由申请人提供）：本R21/R33申请是对RFA-DA-09-020的回应。它寻求支持解决RFA推荐的一个研究领域“NIDA临床试验网络（CTN）中多个试验的次要数据分析”。通过利用CTN来自多个试验的多地点治疗数据的财富，我们寻求，在R21阶段（第一年），1)从原始试验数据（堆叠格式）中识别和创建可分析的平面数据文件，2)通过为多个标准化临床仪器编程评分算法生成研究变量和代码本，3)评估缺失数据的程度，以及4)检查基线临床概况(药物使用和诊断，心理健康，和生活质量)，以指导R33阶段的工作。在R33阶段（2-4年），我们计划评估物质使用诊断的分类模型和维度模型的构建、并发性和预测有效性。多站点CTN试验提供了一个无与伦比的绝佳机会来探索和评估这些与治疗结果和新出现的诊断和统计手册版本- iv （DSM-V）直接相关的临床和科学重要问题。R33的研究目的是：1)研究DSM-IV诊断问题按性别和种族/民族的测量等效性；2)使用诊断分类的分类、维度和综合混合模型评估DSM-IV物质使用诊断的构建和并发效度；3)确定经验定义的药物依赖亚型（综合混合模型）预测后续结果，并探索关联的中介和调节因子。4)评估经验定义的共病药物依赖亚型对预测后续结局的影响，并探讨相关的中介和调节因子。在所有分析中，将通过分层分析或酌情纳入相互作用术语来检查性别/性别差异。

DESCRIPTION (provided by applicant): This R21/R33 application is in response to RFA-DA-09-020. It seeks support to address one research area recommended by this RFA "Secondary data analyses of multiple trials in the NIDA Clinical Trials Network (CTN)." By capitalizing on the CTN's wealth of multisite treatment data from multiple trials, we seek to, in the R21 phase (Year 1), 1) identify and create analyzable flat data files from the raw trial data (stacked format), 2) generate study variables and codebooks by programming scoring algorithms for multiple standardized clinical instruments, 3) evaluate the extent of missing data, and 4) examine baseline clinical profiles (substance use and diagnoses, mental health, and quality of life) of trial participants by gender and race/ethnicity in order to guide the R33 phase of work. In the R33 phase (Years 2-4), we plan to evaluate the construct, concurrent, and predictive validity of categorical and dimensional models of substance use diagnoses. The multisite CTN trials present an unparalleled, excellent opportunity to explore and evaluate these clinically and scientifically important issues directly related to treatment outcomes and to the emerging Diagnostic and Statistical Manual Version-IV (DSM-V). Study Aims for R33 are to 1) investigate measurement equivalence of DSM-IV diagnostic questions by gender and race/ethnicity, 2) evaluate the construct and concurrent validity of DSM-IV substance use diagnoses using categorical, dimensional, and integrated hybrid models of diagnostic classification, 3) determine empirically defined drug dependence subtypes (an integrated hybrid model) in predicting subsequent outcomes and to explore the mediator and moderators of the associations, and 4) evaluate empirically-defined comorbid drug dependence subtypes in predicting subsequent outcomes and to explore the mediator and moderators of the associations. Gender/sex differences will be examined in all analyses either through stratified analyses or the inclusions of interaction terms as appropriate. PUBLIC HEALTH RELEVANCE: Randomized clinical trials dominate treatment research today. Findings from the proposed study will make unique contributions to randomized addiction treatment research. They will help inform gender and racial/ethnic differences in the clinical profiles of substance use diagnoses, the quality and clinical utility of self-report diagnostic measures, interpretations of study results within the CTN, and future designs and analyses of addiction treatment trials.