chest_lm: Assessing confounding effects using Linear regression models

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

Assessing confounding effects using Linear regression models

Description

'chest_lm' is used to assess confounding effects using Linear Regression Models. It presents linear regression coefficients as effect estimates and changes when other variables are added sequentially to the model.

Usage

chest_lm(
  crude,
  xlist,
  data,
  method = "qr",
  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.
method	The method to be used; see 'lm'.
na_omit	Remove all missing values.
indicate	indicate progress
plus	Change the + sign before variable names.
...	Further optional arguments.

Value

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

See Also

'lm' of 'stats'

Examples

vlist <- c("Age", "Sex", "Married", "Cancer", "CVD", "Education", "Income")
chest_lm(crude = "BMI ~ Diabetes", xlist = vlist, data = diab_df, na_omit = TRUE)

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