Larsen K, Petersen J H, Budtz-Jørgensen E, Endahl L
Department of Biostatistics, University of Copenhagen, Denmark.
Biometrics. 2000 Sep;56(3):909-14. doi: 10.1111/j.0006-341x.2000.00909.x.
Logistic regression with random effects is used to study the relationship between explanatory variables and a binary outcome in cases with nonindependent outcomes. In this paper, we examine in detail the interpretation of both fixed effects and random effects parameters. As heterogeneity measures, the random effects parameters included in the model are not easily interpreted. We discuss different alternative measures of heterogeneity and suggest using a median odds ratio measure that is a function of the original random effects parameters. The measure allows a simple interpretation, in terms of well-known odds ratios, that greatly facilitates communication between the data analyst and the subject-matter researcher. Three examples from different subject areas, mainly taken from our own experience, serve to motivate and illustrate different aspects of parameter interpretation in these models.
具有随机效应的逻辑回归用于研究非独立结果情况下解释变量与二元结果之间的关系。在本文中，我们详细研究了固定效应参数和随机效应参数的解释。作为异质性度量，模型中包含的随机效应参数不易解释。我们讨论了不同的异质性替代度量，并建议使用中位数优势比度量，它是原始随机效应参数的函数。该度量允许根据著名的优势比进行简单解释，这极大地促进了数据分析师与主题研究人员之间的交流。来自不同主题领域的三个例子，主要取自我们自己的经验，用于激发和说明这些模型中参数解释的不同方面。
