Description Usage Arguments Value References Examples

Estimation of natural direct and indirect effects for generalized linear models. The function utilizes a data-duplication algorithm to fit marginal and conditional GLMs in a way that allow for consistent variance estimation. The function produces point estimates, confidence intervals and p-values for the natural indirect effect and the mediation proportion

1 2 3 |

`formula` |
A formula expression as for
other regression models, of the form response ~ predictors. See the documentation of |

`exposure` |
The exposure (string). |

`mediator` |
The mediator (string). |

`df` |
A name of a data frame where all variables mentioned in formula are stored. |

`family` |
A |

`corstr` |
A working correlation structure. See |

`conf.level` |
Confidence level for all confidence intervals (default 0.95) |

`surv` |
Is the outcome survival (not supported) |

`pres` |
Presentation of the coefficient tables. "tog" for a single table, "sep" for two separated tables. |

`niealternative` |
Alternative hypothesis for testing that the nie=0. Either "two-sided" (default) or "one-sided" for alternative nie>0. |

`...` |
Further arguments for the |

The output contains the following components:

`call` |
The call. |

`GEE.fit` |
Results of fitting the GEE for the duplicated data. |

`nie` |
The natural indirect effect estimate. NIE and NDE are reported on the coefficient scale |

`nie.pval` |
P-value for tesing mediation using the NIE. |

`nde` |
The natural direct effect estimate. |

`nie.ci` |
Confidence interval in for the NIE in confidence level conf.level. |

`pm` |
The mediation proportion estimate. |

`pm.pval` |
P-value for tesing one-sided mediation using the mediation proportion. |

`pm.ci` |
Confidence interval for the mediation proportion in confidence level conf.level. |

Nevo, Liao and Spiegelman, *Estimation and infernece for the mediation proportion* (2017+)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Not run:
SimNormalData <- function(n,beta1.star = 1, p = 0.3, rho =0.4, inter = 0)
{
beta2 <- (p/rho)*beta1.star
beta1 <- (1-p)*beta1.star
XM <- MASS::mvrnorm(n, mu = c(0,0), Sigma = matrix(c(1,rho,rho,1),2,2))
X <- XM[,1]
M <- XM[,2]
beta <- c(inter, beta1, beta2)
print(beta)
Y <- cbind(rep(1,n),XM)%*%beta+rnorm(n,0,sd = 1)
return(data.frame(X = X, M = M, Y = Y))
}
set.seed(314)
df <- SimNormalData(500)
GEEmediate(Y ~ X + M, exposure = "X", mediator = "M", df = df)
## End(Not run)
``` |

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