000 02041nam a22002177a 4500
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020 _a9780367365479
082 _a519.5
_bYU
100 _aYu, Qingzhao
_913932
245 _aStatistical methods for mediation, confounding and moderation analysis using R and SAS
260 _bCRC Press
_aBoca Raton
_c2022
300 _axv, 277 p.
365 _aGBP
_b130.00
490 _aChapman & Hall/CRC Biostatistics Series
520 _aThird-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 (https://www.routledge.com/Statistical-Methods-for-Mediation-Confounding-and-Moderation-Analysis/Yu-Li/p/book/9780367365479#)
650 _aStatistics - Methodology
_915049
650 _aStatistics - Data processing
_915050
700 _aLi, Bin
_915051
942 _cBK
_2ddc
999 _c5664
_d5664