`R/05_calc_myreg_mediation_analysis.R`

`calc_myreg.Rd`

This function returns functions that can be used to calculate the causal effect measures, given the mediator model fit (`mreg_fit`

) and the outcome model fit (`yreg_fit`

).

```
calc_myreg(
mreg,
mreg_fit,
yreg,
yreg_fit,
avar,
mvar,
cvar,
emm_ac_mreg,
emm_ac_yreg,
emm_mc_yreg,
interaction
)
```

- mreg
A character vector of length 1. Mediator regression type:

`"linear"`

or`"logistic"`

.- mreg_fit
Model fit from

`fit_mreg`

- yreg
A character vector of length 1. Outcome regression type:

`"linear"`

,`"logistic"`

,`"loglinear"`

,`"poisson"`

,`"negbin"`

,`"survCox"`

,`"survAFT_exp"`

, or`"survAFT_weibull"`

.- yreg_fit
Model fit from

`fit_yreg`

- avar
A character vector of length 1. Treatment variable name.

- mvar
A character vector of length 1. Mediator variable name.

- cvar
A character vector of length > 0. Covariate names. Use

`NULL`

if there is no covariate. However, this is a highly suspicious situation. Even if`avar`

is randomized,`mvar`

is not. Thus, there are usually some confounder(s) to account for the common cause structure (confounding) between`mvar`

and`yvar`

.- emm_ac_mreg
A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the mediator model.

- emm_ac_yreg
A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the outcome model.

- emm_mc_yreg
A character vector of length > 0. Effect modifiers names. The covariate vector in mediator-covariate product term in outcome model.

- interaction
A logical vector of length 1. The presence of treatment-mediator interaction in the outcome model. Default to TRUE.

A list containing two functions. The first is for calculating point estimates. The second is for calculating the correspoding