| 胡纯严,胡良平.合理进行多重Logistic回归分析——结合多水平模型分析[J].四川精神卫生杂志,2022,35(6):500-505.Hu Chunyan,Hu Liangping,Reasonably conduct the multiple Logistic regression analysis combined with the multilevel model analysis[J].SICHUAN MENTAL HEALTH,2022,35(6):500-505 |
| 合理进行多重Logistic回归分析——结合多水平模型分析 |
| Reasonably conduct the multiple Logistic regression analysis combined with the multilevel model analysis |
| 投稿时间：2022-11-13 |
| DOI：10.11886/scjsws20221113003 |
| 中文关键词: 层级结构 分层变量 多水平模型 随机截距 随机斜率 |
| 英文关键词:Hierarchical structure Hierarchical variable Multilevel model Random intercept Random slope |
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| 中文摘要: |
| 本文目的是介绍如何结合多水平模型分析，合理地进行多重Logistic回归分析的方法。第一，介绍了与多水平模型分析有关的4个基本概念。第二，介绍了构建多水平模型的3个步骤。第三，通过一个多中心药物临床试验的实例，介绍了如何用SAS软件进行分析的全过程，其内容如下：①检验各中心优势比之间是否具有齐性；②对试验中心产生哑变量后构建多重Logistic回归模型；③将试验中心视为分层变量构建多重Logistic回归模型；④构建随机截距多水平多重Logistic回归模型；⑤构建随机截距和随机斜率多水平多重Logistic回归模型。得到的结论是，当具有二值结果变量的各层级资料间存在差异时，最合适的做法是构建多水平多重Logistic回归模型。 |
| 英文摘要: |
| The purpose of this paper was to introduce how to reasonably analyze the multiple Logistic regression models in combination with the multilevel model analysis. Firstly， four basic concepts related to the multilevel model analysis were introduced. Secondly， three steps for building a multilevel model were given. Thirdly， through an example of a multicenter drug clinical trial， the whole process of how to use SAS software for the analysis was presented. The contests were as follows： ① testing whether the odds ratios of each center were homogenous； ② building the multiple Logistic regression model after generating dummy variables for the trial center； ③ constructing a multiple Logistic regression model with the trial center as a stratified variable； ④ building a random intercept multilevel multiple Logistic regression model； ⑤ constructing a random intercept and random slope multilevel multiple Logistic regression model. The conclusion was that when there were differences among the data at different hierarchies with binary outcome variables， the most appropriate approach was to build a multilevel multiple Logistic regression model. |
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