Del i serien Chapman & Hall/CRC Biostatistics Series

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

Häftad, Engelska, 2024

AvQingzhao Yu,Bin Li

879 kr

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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 analysisMultivariate and Multiple third-variable effect analysisMultilevel mediation/confounding analysisThird-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysisR packages and SAS macros to implement methods proposed in the book

Produktinformation

Utgivningsdatum2024-05-27
Mått156 x 234 x 21 mm
Vikt540 g
FormatHäftad
SpråkEngelska
SerieChapman & Hall/CRC Biostatistics Series
Antal sidor294
FörlagTaylor & Francis Ltd
ISBN9781032220086

Tillhör följande kategorier

Matematisk statistik inom Naturvetenskap och teknik

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