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

Shi Huang (), David P. MacKinnon, Tatiana Perrino, Carlos Gallo, Gracelyn Cruden and C. Hendricks Brown ()
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Shi Huang: Vanderbilt University
David P. MacKinnon: Arizona State University
Tatiana Perrino: University of Miami Miller School of Medicine
Carlos Gallo: Northwestern University
Gracelyn Cruden: Northwestern University
C. Hendricks Brown: Northwestern University

Statistical Methods & Applications, 2016, vol. 25, issue 4, No 3, 565-579

Abstract: 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.

Keywords: Mediation; Multiple trials; Data synthesis; Restricted maximum likelihood; R (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10260-016-0354-y

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