Statistical methods for mediation, confounding and moderation analysis using R and SAS
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Bibliographic Information
Statistical methods for mediation, confounding and moderation analysis using R and SAS
（Chapman & Hall/CRC biostatistics series）
CRC Press, 2022
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Content Type: text (ncrcontent), Media Type: unmediated (ncrmedia), Carrier Type: volume (ncrcarrier)
Includes bibliographical references and index
Summary: "Third-variable effect refers to the intervening effect of a third-variable on the observed relationship between an exposure and an outcome. The third-variable effect analysis differentiates the effect from multiple third variables that explain the established exposure-outcome relationship. Depending on whether there is a causal relationship from the exposure to the third variable to the outcome, the third-variable effect can be categorized into two major groups: mediation effect where a causal relationship is assumed and confounding effect where there is no causal relationship. A causal relationship can be established through randomized experiments"-- Provided by publisher
Contents of Works
- A review of third-variable effect inferences
- Advanced statistical modeling and machine learning methods used in the book
- The general third-variable effect analysis method
- The implementation of general third-variable effect analysis method
- Assumptions for the general third-variable analysis
- Multiple exposures and multivariate responses
- Regularized third-variable effect analysis for high-dimensional dataset
- Interaction/moderation analysis with third-variable effects
- Third-variable effect analysis with multilevel additive models
- Bayesian third-variable effect analysis
- Other issues
