Statistical methods for mediation, confounding and moderation analysis using R and SAS / Qingzhao Yu and Bin Li.

Author/creator Yu, Qingzhao
Other author Li, Bin (Professor of statistics)
Format Electronic
EditionFirst edition.
Publication InfoBoca Raton : CRC Press, 2022.
Descriptionpages cm.
Supplemental ContentFull text available from Taylor & Francis eBooks
Subjects

SeriesStatistics
Contents 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.
Abstract "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.
Bibliography noteIncludes bibliographical references and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2021046570
ISBN9780367365479 (hardback)
ISBN9781032220086 (paperback)
ISBN(ebook)

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