LATENT CLASS GROWTH MODELING IN DRUG ABUSE RESEARCH

治疗研究中心竞争性更新的这一组成部分旨在使用和开发潜在类别增长模型（LCGM），以分析复杂患者行为随时间的变化。该组件的总体目标是应用该方法更好地表征复杂患者的药物使用行为，并更好地了解导致这些模式相似性和异质性的潜在因素。这项工作的目的是帮助回答问题，如：研究参与者谁复发一次从那些复发两次或三次不同？哪些基线因素影响吸烟者最有可能采取的潜在轨迹？情绪或先前的酒精使用是否会影响轨迹的路径或形状？这项工作适用于多个中心的组成部分，重点是尼古丁，但不限于一种药物。因此，它旨在帮助我们比较和对比不同治疗和药物滥用的行为轨迹。这项工作是围绕满足四个目标：（1）应用LCGM以前收集的数据，以补充和扩展这些研究的结果，并检查LCGM在药物滥用研究中的适用性，可解释性和有用性。(2)使用计算机模拟研究样本量的各个方面对LCGM捕获潜在轨迹的能力的影响。(3)为了揭示吸烟变化的潜在轨迹， 在两个提议的TRC戒烟临床试验中饮酒。(4)确定解释和数据显示的最佳方法。

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.