Literature DB >> 29497920

Identification and Sensitivity Analysis for Average Causal Mediation Effects with Time-Varying Treatments and Mediators: Investigating the Underlying Mechanisms of Kindergarten Retention Policy.

Soojin Park1, Peter M Steiner2, David Kaplan2.   

Abstract

Considering that causal mechanisms unfold over time, it is important to investigate the mechanisms over time, taking into account the time-varying features of treatments and mediators. However, identification of the average causal mediation effect in the presence of time-varying treatments and mediators is often complicated by time-varying confounding. This article aims to provide a novel approach to uncovering causal mechanisms in time-varying treatments and mediators in the presence of time-varying confounding. We provide different strategies for identification and sensitivity analysis under homogeneous and heterogeneous effects. Homogeneous effects are those in which each individual experiences the same effect, and heterogeneous effects are those in which the effects vary over individuals. Most importantly, we provide an alternative definition of average causal mediation effects that evaluates a partial mediation effect; the effect that is mediated by paths other than through an intermediate confounding variable. We argue that this alternative definition allows us to better assess at least a part of the mediated effect and provides meaningful and unique interpretations. A case study using ECLS-K data that evaluates kindergarten retention policy is offered to illustrate our proposed approach.

Keywords:  causal mediation analysis; heterogeneous effects; homogeneous effects; sensitivity analysis; time-varying confounding variable; time-varying treatment and mediator

Mesh:

Year:  2018        PMID: 29497920     DOI: 10.1007/s11336-018-9606-0

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  11 in total

1.  Marginal structural models and causal inference in epidemiology.

Authors:  J M Robins; M A Hernán; B Brumback
Journal:  Epidemiology       Date:  2000-09       Impact factor: 4.822

2.  Theory and Analysis of Total, Direct, and Indirect Causal Effects.

Authors:  Axel Mayer; Felix Thoemmes; Norman Rose; Rolf Steyer; Stephen G West
Journal:  Multivariate Behav Res       Date:  2014 Sep-Oct       Impact factor: 5.923

3.  Counterfactual graphical models for longitudinal mediation analysis with unobserved confounding.

Authors:  Ilya Shpitser
Journal:  Cogn Sci       Date:  2013-07-30

4.  Flexible Mediation Analysis With Multiple Mediators.

Authors:  Johan Steen; Tom Loeys; Beatrijs Moerkerke; Stijn Vansteelandt
Journal:  Am J Epidemiol       Date:  2017-07-15       Impact factor: 4.897

5.  Mediation Analysis with Multiple Mediators.

Authors:  T J VanderWeele; S Vansteelandt
Journal:  Epidemiol Methods       Date:  2014-01

6.  Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement.

Authors:  Machteld Vandecandelaere; Stijn Vansteelandt; Bieke De Fraine; Jan Van Damme
Journal:  Multivariate Behav Res       Date:  2016-04-19       Impact factor: 5.923

7.  Mediation analysis with time varying exposures and mediators.

Authors:  Tyler J VanderWeele; Eric J Tchetgen Tchetgen
Journal:  J R Stat Soc Series B Stat Methodol       Date:  2016-06-27       Impact factor: 4.488

8.  Identifying Causal Estimands for Time-Varying Treatments Measured with Time-Varying (Age or Grade-Based) Instruments.

Authors:  Peter M Steiner; Soojin Park; Yongnam Kim
Journal:  Multivariate Behav Res       Date:  2016-08-19       Impact factor: 5.923

9.  Causal mediation analysis with multiple mediators.

Authors:  R M Daniel; B L De Stavola; S N Cousens; S Vansteelandt
Journal:  Biometrics       Date:  2014-10-28       Impact factor: 2.571

10.  Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens.

Authors:  Bianca L De Stavola; Rhian M Daniel; George B Ploubidis; Nadia Micali
Journal:  Am J Epidemiol       Date:  2014-12-11       Impact factor: 4.897

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