Qingzhao Yu, Bin Li (Louisiana State University, LA, USA)

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

• Broschiertes Buch

Produktbeschreibung

This book introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis.

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Produktdetails

• Chapman & Hall/CRC Biostatistics Series
• Verlag: Taylor & Francis Ltd
• Seitenzahl: 294
• Erscheinungstermin: 27. Mai 2024
• Englisch
• Abmessung: 234mm x 156mm x 16mm
• Gewicht: 450g
• ISBN-13: 9781032220086
• ISBN-10: 1032220082
• Artikelnr.: 70349550

Autorenporträt

Qingzhao Yu is Professor in Biostatistics, Louisiana State University Health Sciences Center. Bin Li is Associate Professor in Statistics, Louisiana State University.

Inhaltsangabe

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

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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

Rezensionen

"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 Adatorwovor, College of Public health University of Kentucky, USA, ISCB, April 2023