Bmc Medical Research Methodology Open Access beyond Logistic Regression: Structural Equations Modelling for Binary Variables and Its Application to Investigating Unobserved Confounders
Bmc Medical Research Methodology Open Access beyond Logistic Regression: Structural Equations Modelling for Binary Variables and Its Application to Investigating Unobserved Confounders
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Bmc 医学研究方法论超越逻辑回归的开放获取：二元变量的结构方程模型及其在调查未观察到的混杂因素中的应用
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通讯作者：
E. Kupek
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作者：
E. Kupek
背景：结构方程模型（SEM）已越来越多地用于医学统计学解决相关的回归方程系统。然而，其更广泛使用的一个巨大障碍是它在广义线性模型的框架内处理分类变量的困难。
Background: Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models.