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

点击翻译按钮获取中文摘要

ABSTRACT 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.