Longitudinal Methods for Smoking Cessation Research

描述（由申请人提供）：拟议的研究项目的目的是利用统计学上先进的技术建模的纵向数据更容易在药物滥用预防和干预领域的应用研究人员。为此，该项目旨在展示潜在增长模型在分析纵向物质使用数据方面的用途和有用性。提出了三个目标：（1）在分析两个药物使用预防数据集时，将潜在增长模型的使用与传统上使用的纵向实验设计数据技术的使用进行对比;（二）设计并执行一项模拟研究，以评估2种不同的LGM方法和2种不同的ANOVA方法在以下方面的纵向实验设计数据分析中的统计功效和I类错误率：6种常见的干预效果模式;（3）通过提供相关的、明确的例子，为应用研究者使用这些技术提供指导。拟议的项目将共同为将应用统计和行为研究的进展与实质性研究人员采用的数据分析方法更密切地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.