000 | 02041nam a22002177a 4500 | ||
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005 | 20240206142512.0 | ||
008 | 240206b |||||||| |||| 00| 0 eng d | ||
020 | _a9780367365479 | ||
082 |
_a519.5 _bYU |
||
100 |
_aYu, Qingzhao _913932 |
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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 |
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650 |
_aStatistics - Data processing _915050 |
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700 |
_aLi, Bin _915051 |
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942 |
_cBK _2ddc |
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999 |
_c5664 _d5664 |