fit_yreg: Fit a model for the outcome given the treatment, mediator,...

In regmedint: Regression-Based Causal Mediation Analysis with Interaction and Effect Modification Terms

Fit a model for the outcome given the treatment, mediator, and covariates.

Description

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".

Usage

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

Arguments

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.

Details

The outcome regression functions to be called are the following:

• "linear" lm

• "logistic" glm

• "loglinear" glm (modified Poisson)

• "poisson" glm

• "negbin" glm.nb

• "survCox" coxph

• "survAFT_exp" survreg

• "survAFT_weibull" survreg

Value

Model fit object from on of the above regression functions.

regmedint documentation built on June 25, 2024, 1:16 a.m.