1st Edition

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

By Qingzhao Yu, Bin Li Copyright 2022
294 Pages 100 B/W Illustrations
by Chapman & Hall

294 Pages 100 B/W Illustrations
by Chapman & Hall

294 Pages 100 B/W Illustrations
by Chapman & Hall

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... Read more

1 Introduction  2 A Review of Third-Variable Effect Inferences  3 Advanced Statistical Modeling and Machine Learning Methods Used in the Book  4 The General Third-Variable Effect Analysis Method  5 The Implementation of General Third-Variable Effect Analysis Method  6 Assumptions for the General Third-Variable Analysis  7 Multiple Exposures and Multivariate Responses  8 Regularized Third-Variable Effect Analysis for High-Dimensional Dataset  9 Interaction/Moderation Analysis with Third-Variable Effects  10 Third-Variable Effect Analysis with Multilevel Additive Models  11 Bayesian Third-Variable Effect Analysis  12 Other Issues

Biography

Qingzhao Yu is Professor in Biostatistics, Louisiana State University Health Sciences Center.

Bin Li is Associate Professor in Statistics, Louisiana State University.

"I believe that this book is very handy for not only professionals or early career professionals but also graduate students, and those wanting to be up to speed on the latest techniques for analysis of the effect of the third variable. I will recommend this book without any reservation to individuals in research that frequently see the complications associated with the interpretation of the third variable."

Reuben AdatorwovorCollege of Public health University of Kentucky, USA, ISCB, April 2023