chest_glm: 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 = "  + ",
  ...
)

Value

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

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.

Examples

Run this code

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

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