Hu F B, Goldberg J, Hedeker D, Flay B R, Pentz M A
Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, USA.
Am J Epidemiol. 1998 Apr 1;147(7):694-703. doi: 10.1093/oxfordjournals.aje.a009511.
Several approaches have been proposed to model binary outcomes that arise from longitudinal studies. Most of the approaches can be grouped into two classes: the population-averaged and subject-specific approaches. The generalized estimating equations (GEE) method is commonly used to estimate population-averaged effects, while random-effects logistic models can be used to estimate subject-specific effects. However, it is not clear to many epidemiologists how these two methods relate to one another or how these methods relate to more traditional stratified analysis and standard logistic models. The authors address these issues in the context of a longitudinal smoking prevention trial, the Midwestern Prevention Project. In particular, the authors compare results from stratified analysis, standard logistic models, conditional logistic models, the GEE models, and random-effects models by analyzing a binary outcome from two and seven repeated measurements, respectively. In the comparison, the authors focus on the interpretation of both time-varying and time-invariant covariates under different models. Implications of these methods for epidemiologic research are discussed.
已经提出了几种方法来对纵向研究中出现的二元结局进行建模。大多数方法可以分为两类：总体平均法和个体特定法。广义估计方程（GEE）方法通常用于估计总体平均效应，而随机效应逻辑模型可用于估计个体特定效应。然而，许多流行病学家并不清楚这两种方法之间的关系，也不清楚这些方法与更传统的分层分析和标准逻辑模型之间的关系。作者在一项纵向吸烟预防试验——中西部预防项目的背景下解决了这些问题。具体而言，作者分别通过分析两次和七次重复测量的二元结局，比较了分层分析、标准逻辑模型、条件逻辑模型、GEE模型和随机效应模型的结果。在比较中，作者关注不同模型下随时间变化和不随时间变化的协变量的解释。讨论了这些方法对流行病学研究的意义。
