`R/04_fit_yreg_outcome_modeling.R`

`fit_yreg.Rd`

The outcome model type `yreg`

can be one of the following `"linear"`

, `"logistic"`

, `"loglinear"`

(implemented as modified Poisson), `"poisson"`

, `"negbin"`

, `"survCox"`

, `"survAFT_exp"`

, or `"survAFT_weibull"`

.

```
fit_yreg(
yreg,
data,
yvar,
avar,
mvar,
cvar,
emm_ac_yreg = NULL,
emm_mc_yreg = NULL,
eventvar,
interaction
)
```

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

`"linear"`

,`"logistic"`

,`"loglinear"`

,`"poisson"`

,`"negbin"`

,`"survCox"`

,`"survAFT_exp"`

, or`"survAFT_weibull"`

.- data
Data frame containing the following relevant variables.

- yvar
A character vector of length 1. Outcome variable name. It should be the time variable for the survival outcome.

- 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_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.

- eventvar
An character vector of length 1. Only required for survival outcome regression models. Note that the coding is 1 for event and 0 for censoring, following the R survival package convention.

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

Model fit object from on of the above regression functions.