chest_glm: Assessing confounding effects using Generalized Linear...

In chest: Change-in-Estimate Approach to Assess Confounding Effects

Assessing confounding effects using Generalized Linear regression models

Description

Please note: 'chest_glm' is used to assess confounding effects using Generalized Linear Models, such as logistic regression and Poisson regression with 'glm'. It presents odds ratios or rate ratios for the association between exposure and outcome variables by adding other variables (potential confounders) to the model sequentially. The order of variables to be added is based on the magnitudes of the changes in effect estimates.

Usage

chest_glm(
  crude,
  xlist,
  data,
  family = "binomial",
  method = "glm.fit",
  na_omit = TRUE,
  indicate = FALSE,
  plus = "  + ",
  ...
)

Arguments

crude	An object of formula for initial model, generally crude model. However, any other variables can also be included here as the initial model.
xlist	A vector of characters with all variable names of potential confounders.
data	Data frame.
family	Description of the error distribution. Default is "binomial".
method	Method to detect for singularity.
na_omit	Remove all missing values, default: 'na_omit = TRUE'.
indicate	indicate progress.
plus	Change the + sign before variable names.
...	Further optional arguments for forestplot.

Value

A table with effect estimates and their changes at all steps.

See Also

'glm' 'stats'

Examples

chest_glm(
  crude = "Endpoint ~ Diabetes", xlist = c("Age", "Sex", "Married"),
  na_omit = TRUE, data = diab_df
)

chest documentation built on March 31, 2023, 10:02 p.m.