## 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 language | English (US) |
---|---|

Pages (from-to) | 565-579 |

Number of pages | 15 |

Journal | Statistical Methods and Applications |

Volume | 25 |

Issue number | 4 |

DOIs | |

State | Published - Nov 1 2016 |

## Keywords

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

## ASJC Scopus subject areas

- Statistics and Probability
- Statistics, Probability and Uncertainty