STATISTICAL SUPPORT/DATA MANAGEMENT CORE

统计支持和数据管理（SS&DM）核心将通过促进使用先进的统计方法来分析纵向数据以及经济有效地获取和管理纵向数据，促进推进纵向药物滥用研究中心的主题。具体目标是：1)为中心研究人员提供与药物滥用及其与药物治疗和其他服务系统相互作用的纵向研究相关的分析问题的统计咨询；2)制定和实施纵向数据分析统计教育计划，专门用于药物滥用研究应用；3)对多个研究合并和/或比较的数据进行分析

The Statistical Support and Data Management (SS&DM) Core will promote the theme of the Center for Advancing Longitudinal Drug Abuse Research by facilitating the use of advanced statistical methods for analysis of longitudinal data and the cost-effective acquisition and management of longitudinal data. Specific aims are: 1) provide statistical consultation to Center researchers on analysis issues specifically relevant to longitudinal research on drug abuse and its interaction with drug treatment and other service systems; 2) develop and deliver a program of statistical education on analysis of longitudinal data, specialized to drug abuse research applications; 3) conduct analysis of data combined and/or compared across multiple studies in order to develop, expand, compare, and validate complex models; 4) provide consultation on strategies for the efficient acquisition and processing of high quality data; and 5) promote the accessibility and comparability of data acquired by Center projects. Core activities will include consultation to Center-supported and collaborating researchers on statistical and data-related issues; development of procedures, examples and applications to support consultation; workshops for statisticians, researchers, and consumers of research results; and cross-project analyses. Analytic methods will include generalized linear/non-linear models and structural equation modeling with latent variables for growth curves, multi-level, and multiple group models. Core staff will work with Center and collaborating researchers to identify and refine research questions and hypotheses related to drug use trajectories and their interaction with service systems such as drug treatment, criminal justice, and mental health. Efforts should lead to improved application of analytic methods, better data quality, more effective use of research results, and increased productivity.