LATENT CLASS GROWTH MODELING IN DRUG ABUSE RESEARCH

药物滥用研究中的潜在类别增长模型

• 批准号: 6831480
• 负责人: Kevin L. Delucchi
• 金额: $ 9.35万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6831480
• 关键词: alcoholism /alcohol abuse; behavior therapy; behavioral /social science research tag; clinical trials; computer simulation; data management; emotions; human data; human therapy evaluation; mathematical model; model design /development; nicotine; relapse /recurrence; smoking; smoking cessation; statistics /biometry; substance abuse related behavior; substance abuse related disorder

This component of the Treatment Research Center's competitive renewal is designed to both use and develop Latent Class Growth Models (LCGM) for analyzing the change over time in complex patients' behavior. The overall goal of this component is to apply this methodology to better characterize complex patients' drug using behaviors and to better understand the underlying factors that account for both the similarities and heterogeneity in those patterns. This work is designed to help answer questions such as; Are study participants who relapse once different from those who relapse two or three times? What baseline factors influence which latent trajectory a smoker will most likely take? Does mood or prior alcohol use influence the path or shape of the trajectory taken? The work applies to multiple Center components which focus on nicotine but is not limited to one drug. Thus, it is aimed at helping us compare and contrast trajectories of behavior across treatments and drugs of abuse. The work is organized around meeting four aims; (1) To apply LCGM to previously collected data to compliment and extend the findings of those studies and to examine the applicability, interpretability, and usefulness of LCGM in drug abuse research. (2) To use computer simulations to study the effects of various aspects of sample size on the ability of LCGM to capture the latent trajectories using studies based on computer simulation. (3) To uncover the latent trajectories of change in smoking and alcohol consumption in the two proposed TRC clinical trials of smoking cessation. (4) To determine optimal methods of interpretation and data display.

治疗研究中心竞争性更新的组成部分旨在使用和开发潜在的类增长模型（LCGM），以分析复杂患者行为的随着时间的变化。该组成部分的总体目标是应用这种方法，以更好地表征复杂的患者使用行为，并更好地理解这些模式中相似性和异质性的潜在因素。这项工作旨在帮助回答诸如：复发的研究参与者是否与那些复发的人有所不同？哪些基线因素会影响吸烟者最有可能采用哪种潜在轨迹？情绪或先前的酒精使用会影响所采用的轨迹的路径或形状吗？这项工作适用于以尼古丁为重点但不限于一种药物的多个中心成分。因此，它旨在帮助我们比较跨虐待和虐待药物的行为轨迹和对比轨迹。这项工作是围绕四个目标组织的。 （1）将LCGM应用于先前收集的数据，以补充和扩展这些研究的发现，并检查LCGM在药物滥用研究中的适用性，可解释性和实用性。 （2）使用计算机模拟研究样本量的各个方面对LCGM使用基于计算机模拟的研究捕获潜在轨迹的能力的影响。 （3）揭示吸烟变化的潜在轨迹 在两项拟议的TRC戒烟临床试验中，饮酒。 （4）确定最佳解释和数据显示方法。