chest (version 0.3.7)

chest_clogit: 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,
  ...
)

Value

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

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.

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
)