Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS

di Qingzhao Yu (Autore)

Bin Li (Autore)

Taylor & Francis Ltd, 2024

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

• Autore: Qingzhao Yu, Bin Li
• Editore: Taylor & Francis Ltd
• Collana: Chapman & Hall/CRC Biostatistics Series
• Anno: 2024
• Rilegatura: Paperback / softback
• Pagine: 294 p.
• Dimensioni: 234 x 156 mm
• Peso: 540 gr.
• EAN: 9781032220086

Testo in English