Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS
暫譯: 使用 R 和 SAS 的中介、混淆和調節分析的統計方法
Yu, Qingzhao, Li, Bin
相關主題
商品描述
Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers.
Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Using this method, multiple third- variables of different types can be considered simultaneously, and the indirect effect carried by individual third-variables can be separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis.
Key Features:
- Parametric and nonparametric method in third variable analysis
- Multivariate and Multiple third-variable effect analysis
- Multilevel mediation/confounding analysis
- Third-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysis
- R packages and SAS macros to implement methods proposed in the book
商品描述(中文翻譯)
第三變數效應是指介入暴露變數與反應變數之間關係的第三變數所傳遞的效應。區分多個第三變數的個別因素的間接效應，對於現代研究者來說是一個持續的問題。
《使用 R 和 SAS 進行中介、混淆和調節分析的統計方法》介紹了適用於各種不同類型的反應（類別或連續）、暴露或第三變數的第三變數效應的一般定義。使用這種方法，可以同時考慮多種類型的第三變數，並將個別第三變數所承載的間接效應與總效應分開。所有熟悉入門統計的學科讀者都會發現這是進行分析的寶貴資源。
主要特點：
- 第三變數分析中的參數和非參數方法
- 多變量和多重第三變數效應分析
- 多層次中介/混淆分析
- 高維數據的第三變數效應分析
- 第三變數分析中的調節/互動效應分析
- 實現書中提出的方法的 R 套件和 SAS 巨集
作者簡介
Qingzhao Yu is Professor in Biostatistics, Louisiana State University Health Sciences Center.
Bin Li is Associate Professor in Statistics, Louisiana State University.
作者簡介(中文翻譯)
Qingzhao Yu 是路易斯安那州立大學健康科學中心的生物統計學教授。
Bin Li 是路易斯安那州立大學的統計學副教授。
