A statistical method for synthesizing mediation analyses using the product of coefficient approach across multiple trials

Shi Huang, David Mackinnon, Tatiana Perrino, Carlos Gallo, Gracelyn Cruden, C. Hendricks Brown

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Mediation analysis often requires larger sample sizes than main effect analysis to achieve the same statistical power. Combining results across similar trials may be the only practical option for increasing statistical power for mediation analysis in some situations. In this paper, we propose a method to estimate: (1) marginal means for mediation path a, the relation of the independent variable to the mediator; (2) marginal means for path b, the relation of the mediator to the outcome, across multiple trials; and (3) the between-trial level variance–covariance matrix based on a bivariate normal distribution. We present the statistical theory and an R computer program to combine regression coefficients from multiple trials to estimate a combined mediated effect and confidence interval under a random effects model. Values of coefficients a and b, along with their standard errors from each trial are the input for the method. This marginal likelihood based approach with Monte Carlo confidence intervals provides more accurate inference than the standard meta-analytic approach. We discuss computational issues, apply the method to two real-data examples and make recommendations for the use of the method in different settings.

Original languageEnglish (US)
Pages (from-to)565-579
Number of pages15
JournalStatistical Methods and Applications
Volume25
Issue number4
DOIs
StatePublished - Nov 1 2016

Keywords

  • Data synthesis
  • Mediation
  • Multiple trials
  • R
  • Restricted maximum likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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