chest_clogit: Assessing confounding effects using conditional logistic...

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

Assessing confounding effects using conditional logistic regression models

Description

'chest_clogit' is used to fit many Conditional Logistic Regression models to assess confounding effects.

Usage

chest_clogit(
  crude,
  xlist,
  data,
  method = "exact",
  na_omit = TRUE,
  plus = "  + ",
  indicate = FALSE,
  ...
)

Arguments

crude	An object of formula for the 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	See 'clogit', default is the "exact" method.
na_omit	Remove all missing values, default: 'na_omit = TRUE'.
plus	Change the + sign before variable names.
indicate	indicate the calculation progress.
...	Further optional arguments.

Value

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

See Also

chest

'clogit' in 'survival'

Examples

vlist <- c("Age", "Sex", "Married", "Cancer", "CVD", "Education", "Income")
chest_clogit(
  crude = "Endpoint ~ Diabetes + strata(mid)",
  xlist = vlist, data = diab_df
)

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