### 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) |
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Pages (from-to) | 1-15 |

Number of pages | 15 |

Journal | Statistical Methods and Applications |

DOIs | |

State | Accepted/In press - Feb 25 2016 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Statistics and Probability
- Statistics, Probability and Uncertainty

### Cite this

*Statistical Methods and Applications*, 1-15. https://doi.org/10.1007/s10260-016-0354-y

**A statistical method for synthesizing mediation analyses using the product of coefficient approach across multiple trials.** / Huang, Shi; Mackinnon, David; Perrino, Tatiana; Gallo, Carlos; Cruden, Gracelyn; Brown, C. Hendricks.

Research output: Contribution to journal › Article

*Statistical Methods and Applications*, pp. 1-15. https://doi.org/10.1007/s10260-016-0354-y

}

TY - JOUR

T1 - A statistical method for synthesizing mediation analyses using the product of coefficient approach across multiple trials

AU - Huang, Shi

AU - Mackinnon, David

AU - Perrino, Tatiana

AU - Gallo, Carlos

AU - Cruden, Gracelyn

AU - Brown, C. Hendricks

PY - 2016/2/25

Y1 - 2016/2/25

N2 - 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.

AB - 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.

KW - Data synthesis

KW - Mediation

KW - Multiple trials

KW - R

KW - Restricted maximum likelihood

UR - http://www.scopus.com/inward/record.url?scp=84975721135&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84975721135&partnerID=8YFLogxK

U2 - 10.1007/s10260-016-0354-y

DO - 10.1007/s10260-016-0354-y

M3 - Article

AN - SCOPUS:84975721135

SP - 1

EP - 15

JO - Statistical Methods and Applications

JF - Statistical Methods and Applications

SN - 1618-2510

ER -