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
General
| Imprint: |
Crc Press
|
| Country of origin: |
United Kingdom |
| Series: |
Chapman & Hall/CRC Biostatistics Series |
| Release date: |
February 2022 |
| First published: |
2022 |
| Authors: |
Qingzhao Yu
• Bin Li
|
| Dimensions: |
234 x 156 x 23mm (L x W x T) |
| Format: |
Hardcover
|
| Pages: |
278 |
| ISBN-13: |
978-0-367-36547-9 |
| Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
| LSN: |
0-367-36547-2 |
| Barcode: |
9780367365479 |
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