Hill H A, Schoenbach V J, Kleinbaum D G, Strecher V J, Orleans C T, Gebski V J, Kaplan B H
Division of Epidemiology, School of Public Health, Emory University, Atlanta, GA 30329.
Addict Behav. 1994 Mar-Apr;19(2):159-73. doi: 10.1016/0306-4603(94)90040-x.
Predictors of 7-day abstinence from smoking were identified among participants in a randomized self-help smoking-cessation intervention trial conducted from 1985 to 1988 in Seattle, WA. Subjects were adult smokers belonging to a health maintenance organization who responded to an offer of free quitting assistance. Self-reported smoking status was assessed at 8, 16, and 24 months following enrollment. Predictors of abstinence were identified by longitudinal data analysis using Generalized Estimating Equations (GEEs), a modeling approach which handles repeated-measures data and accommodates time-dependent as well as time-independent covariates. Seventeen items emerged as significant (p < .05) predictors, with odds ratios ranging from 1.3 to 2.1. While much of the previous work in smoking-cessation research has focused on demographic and smoking history variables, results of this study indicate that emphasis should also be placed on psychosocial/motivational factors and quitting activities as important predictors of abstinence. Longitudinal data analysis represents a powerful technique for handling correlated (repeated measures) data, which may prove very useful for future studies of smoking cessation as well as other dynamic processes.
在1985年至1988年于华盛顿州西雅图市开展的一项随机自助戒烟干预试验的参与者中，确定了戒烟7天的预测因素。研究对象是属于一家健康维护组织的成年吸烟者，他们对免费戒烟援助的提议做出了回应。在入组后的8个月、16个月和24个月时评估自我报告的吸烟状况。使用广义估计方程（GEEs）通过纵向数据分析确定了戒烟的预测因素，广义估计方程是一种处理重复测量数据并纳入随时间变化以及不随时间变化的协变量的建模方法。有17项因素成为显著（p < 0.05）预测因素，优势比在1.3至2.1之间。虽然之前戒烟研究的大部分工作都集中在人口统计学和吸烟史变量上，但本研究结果表明，心理社会/动机因素和戒烟活动作为戒烟的重要预测因素也应得到重视。纵向数据分析是处理相关（重复测量）数据的一种强大技术，这可能对未来的戒烟研究以及其他动态过程研究非常有用。
