Literature DB >> 17691077

Mediation analysis via potential outcomes models.

Jeffrey M Albert1.   

Abstract

This paper develops a causal or manipulation model framework for mediation analysis based on the concept of potential outcome. Using this framework, we provide new definitions and measures of mediation. Effects of manipulations are modeled via the linear structural model. Corresponding structural equation models (SEMs), in conjunction with two-stage least-squares estimation and the delta method, are used to perform inference. The methods are applied to data from a study of nursing interventions for postoperative pain. We address the cases of more than two treatment groups, and an interaction among mediators. For the latter, a sensitivity analysis approach to handle unidentified parameters is described. Interpretative advantages of the potential outcomes framework for mediation are emphasized.

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Mesh:

Year:  2008        PMID: 17691077     DOI: 10.1002/sim.3016

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  33 in total

1.  Extended instrumental variables estimation for overall effects.

Authors:  Marshall M Joffe; Dylan Small; Thomas Ten Have; Steve Brunelli; Harold I Feldman
Journal:  Int J Biostat       Date:  2008-04-07       Impact factor: 0.968

2.  Empirical efficiency maximization: improved locally efficient covariate adjustment in randomized experiments and survival analysis.

Authors:  Daniel B Rubin; Mark J van der Laan
Journal:  Int J Biostat       Date:  2008       Impact factor: 0.968

3.  Mediators, moderators, and modulators of causal effects in clinical trials--Dynamically Modified Outcomes (DYNAMO) in health-related quality of life.

Authors:  Gary W Donaldson; Yoshio Nakamura; Carol Moinpour
Journal:  Qual Life Res       Date:  2009-01-20       Impact factor: 4.147

4.  Body image as a mediator of the relationship between body mass index and weight-related quality of life in black women.

Authors:  Tiffany L Cox; Jamy D Ard; T Mark Beasley; Jose R Fernandez; Virginia J Howard; Olivia Affuso
Journal:  J Womens Health (Larchmt)       Date:  2011-08-04       Impact factor: 2.681

5.  Bayesian inference for causal mediation effects using principal stratification with dichotomous mediators and outcomes.

Authors:  Michael R Elliott; Trivellore E Raghunathan; Yun Li
Journal:  Biostatistics       Date:  2010-01-25       Impact factor: 5.899

6.  Tests of Mediation: Paradoxical Decline in Statistical Power as a Function of Mediator Collinearity.

Authors:  T Mark Beasley
Journal:  J Exp Educ       Date:  2014-01-01

7.  Estimation of causal mediation effects for a dichotomous outcome in multiple-mediator models using the mediation formula.

Authors:  Wei Wang; Suchitra Nelson; Jeffrey M Albert
Journal:  Stat Med       Date:  2013-05-06       Impact factor: 2.373

8.  Understanding treatment effect mechanisms of the CAMBRA randomized trial in reducing caries increment.

Authors:  J Cheng; B W Chaffee; N F Cheng; S A Gansky; J D B Featherstone
Journal:  J Dent Res       Date:  2014-10-29       Impact factor: 6.116

9.  Assessing mediation using marginal structural models in the presence of confounding and moderation.

Authors:  Donna L Coffman; Wei Zhong
Journal:  Psychol Methods       Date:  2012-08-20

10.  Power and sample size calculations for evaluating mediation effects in longitudinal studies.

Authors:  Cuiling Wang; Xiaonan Xue
Journal:  Stat Methods Med Res       Date:  2012-12-06       Impact factor: 3.021

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