Longitudinal Methods for Smoking Cessation Research

描述（由申请人提供）：拟议研究项目的目的是使药物滥用预防和干预领域的应用研究人员更容易使用统计复杂技术对纵向数据进行建模。为此，本项目旨在证明潜在增长模型（LGMs）在纵向物质使用数据分析中的使用和有用性。本文提出了三个目标：(1)在两个物质使用预防数据集的分析中，对比使用潜在增长模型和使用更传统的纵向实验设计数据技术；(2)设计并实施模拟研究，评估两种不同LGM方法和两种不同ANOVA方法在纵向实验设计数据分析中对6种常见干预效应模式的统计效力和I型错误率；(3)通过提供相关的、明确的例子，为应用研究人员提供使用这些技术的指导。拟议的项目将共同为更密切地将应用统计和行为研究的进展与实质性研究人员使用的数据分析方法结合起来的努力提供进一步的理由和指导。

DESCRIPTION (provided by applicant): The purpose of the proposed research project is to make the use of statistically sophisticated techniques for the modeling of longitudinal data more accessible to applied researchers in the drug abuse prevention and intervention area. To this end, this project seeks to demonstrate the use and usefulness of latent growth models (LGMs) in the analysis of longitudinal substance use data. Three aims are proposed: (1) To contrast the use of latent growth models with the use of more traditionally used techniques for longitudinal, experimental design data in the analysis of two substance use prevention datasets; (2) To design and execute a simulation study to evaluate the statistical power and the Type I error rate for 2 different LGM approaches and 2 different ANOVA approaches in the analysis of longitudinal experimental design data in regards to 6 commonly occurring intervention effect patterns; and (3) To provide guidance to applied researchers in the use of these techniques by providing relevant, explicit examples. Jointly, the proposed project will provide further rationale and guidance for efforts to more closely integrate advances of applied statistics and behavioral research with the method of data analysis employed by substantive researchers.