Literature DB >> 23997375

Mediation and spillover effects in group-randomized trials: a case study of the 4Rs educational intervention.

Tyler J Vanderweele1, Guanglei Hong, Stephanie M Jones, Joshua L Brown.   

Abstract

Peer influence and social interactions can give rise to spillover effects in which the exposure of one individual may affect outcomes of other individuals. Even if the intervention under study occurs at the group or cluster level as in group-randomized trials, spillover effects can occur when the mediator of interest is measured at a lower level than the treatment. Evaluators who choose groups rather than individuals as experimental units in a randomized trial often anticipate that the desirable changes in targeted social behaviors will be reinforced through interference among individuals in a group exposed to the same treatment. In an empirical evaluation of the effect of a school-wide intervention on reducing individual students' depressive symptoms, schools in matched pairs were randomly assigned to the 4Rs intervention or the control condition. Class quality was hypothesized as an important mediator assessed at the classroom level. We reason that the quality of one classroom may affect outcomes of children in another classroom because children interact not simply with their classmates but also with those from other classes in the hallways or on the playground. In investigating the role of class quality as a mediator, failure to account for such spillover effects of one classroom on the outcomes of children in other classrooms can potentially result in bias and problems with interpretation. Using a counterfactual conceptualization of direct, indirect and spillover effects, we provide a framework that can accommodate issues of mediation and spillover effects in group randomized trials. We show that the total effect can be decomposed into a natural direct effect, a within-classroom mediated effect and a spillover mediated effect. We give identification conditions for each of the causal effects of interest and provide results on the consequences of ignoring "interference" or "spillover effects" when they are in fact present. Our modeling approach disentangles these effects. The analysis examines whether the 4Rs intervention has an effect on children's depressive symptoms through changing the quality of other classes as well as through changing the quality of a child's own class.

Entities:  

Keywords:  Direct/indirect effects; interference; multilevel models; social interactions

Year:  2013        PMID: 23997375      PMCID: PMC3753117          DOI: 10.1080/01621459.2013.779832

Source DB:  PubMed          Journal:  J Am Stat Assoc        ISSN: 0162-1459            Impact factor:   5.033


  8 in total

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Authors:  Mark J van der Laan; Maya L Petersen
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Authors:  Maya L Petersen; Sandra E Sinisi; Mark J van der Laan
Journal:  Epidemiology       Date:  2006-05       Impact factor: 4.822

4.  Toward Causal Inference With Interference.

Authors:  Michael G Hudgens; M Elizabeth Halloran
Journal:  J Am Stat Assoc       Date:  2008-06       Impact factor: 5.033

5.  Marginal structural models for the estimation of direct and indirect effects.

Authors:  Tyler J VanderWeele
Journal:  Epidemiology       Date:  2009-01       Impact factor: 4.822

6.  Direct and indirect effects for neighborhood-based clustered and longitudinal data.

Authors:  T J VanderWeele
Journal:  Sociol Methods Res       Date:  2010-05-01

7.  Two-year impacts of a universal school-based social-emotional and literacy intervention: an experiment in translational developmental research.

Authors:  Stephanie M Jones; Joshua L Brown; J Lawrence Aber
Journal:  Child Dev       Date:  2011-03-10

8.  On causal inference in the presence of interference.

Authors:  Eric J Tchetgen Tchetgen; Tyler J VanderWeele
Journal:  Stat Methods Med Res       Date:  2010-11-10       Impact factor: 3.021

  8 in total
  15 in total

1.  Mediation analysis with multiple versions of the mediator.

Authors:  Tyler J Vanderweele
Journal:  Epidemiology       Date:  2012-05       Impact factor: 4.822

2.  Interference and Sensitivity Analysis.

Authors:  Tyler J VanderWeele; Eric J Tchetgen Tchetgen; M Elizabeth Halloran
Journal:  Stat Sci       Date:  2014-11       Impact factor: 2.901

3.  Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets using Bayesian Principal Stratification.

Authors:  Laura Forastiere; Fabrizia Mealli; Tyler J VanderWeele
Journal:  J Am Stat Assoc       Date:  2016-08-18       Impact factor: 5.033

4.  A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R.

Authors:  Bradley C Saul; Michael G Hudgens
Journal:  J Stat Softw       Date:  2017-11-29       Impact factor: 6.440

5.  Assessing Time-Varying Causal Effect Moderation in Mobile Health.

Authors:  Audrey Boruvka; Daniel Almirall; Katie Witkiewitz; Susan A Murphy
Journal:  J Am Stat Assoc       Date:  2017-03-29       Impact factor: 5.033

6.  Comment.

Authors:  Michael G Hudgens
Journal:  J Am Stat Assoc       Date:  2016-01-15       Impact factor: 5.033

Review 7.  Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.

Authors:  Elizabeth L Turner; Melanie Prague; John A Gallis; Fan Li; David M Murray
Journal:  Am J Public Health       Date:  2017-05-18       Impact factor: 9.308

8.  THE STRATIFIED MICRO-RANDOMIZED TRIAL DESIGN: SAMPLE SIZE CONSIDERATIONS FOR TESTING NESTED CAUSAL EFFECTS OF TIME-VARYING TREATMENTS.

Authors:  Walter Dempsey; Peng Liao; Santosh Kumar; Susan A Murphy
Journal:  Ann Appl Stat       Date:  2020-06-29       Impact factor: 2.083

9.  A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational Studies: The AGReMA Statement.

Authors:  Hopin Lee; Aidan G Cashin; Sarah E Lamb; Sally Hopewell; Stijn Vansteelandt; Tyler J VanderWeele; David P MacKinnon; Gemma Mansell; Gary S Collins; Robert M Golub; James H McAuley; A Russell Localio; Ludo van Amelsvoort; Eliseo Guallar; Judith Rijnhart; Kimberley Goldsmith; Amanda J Fairchild; Cara C Lewis; Steven J Kamper; Christopher M Williams; Nicholas Henschke
Journal:  JAMA       Date:  2021-09-21       Impact factor: 56.272

10.  Measuring What Works: An Impact Evaluation of Women's Groups on Maternal Health Uptake in Rural Nepal.

Authors:  Sheetal Sharma; Edwin van Teijlingen; José M Belizán; Vanora Hundley; Padam Simkhada; Elisa Sicuri
Journal:  PLoS One       Date:  2016-05-23       Impact factor: 3.240

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