Binomial logistic regression multivariable models: finalfit model wrapper

Using finalfit conventions, produces a multivariable binomial logistic regression model for a set of explanatory variables against a binary dependent.

glmmulti(.data, dependent, explanatory, family = "binomial", ...)

Arguments

.data	Data frame.
dependent	Character vector of length 1: name of depdendent variable (must have 2 levels).
explanatory	Character vector of any length: name(s) of explanatory variables.
family	Character vector quoted or unquoted of the error distribution and link function to be used in the model, see glm.
...	Other arguments to pass to glm.

Value

A multivariable glm fitted model.

Details

Uses glm with finalfit modelling conventions. Output can be passed to fit2df.

See also

fit2df, finalfit_merge

Other finalfit model wrappers: coxphmulti(), coxphuni(), crrmulti(), crruni(), glmmixed(), glmmulti_boot(), glmuni(), lmmixed(), lmmulti(), lmuni(), svyglmmulti(), svyglmuni()

Examples

library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"

colon_s %>%
        glmmulti(dependent, explanatory) %>%
        fit2df(estimate_suffix=" (univariable)")
#> Waiting for profiling to be done...
#>             explanatory          OR (univariable)
#> 1 age.factor40-59 years 0.57 (0.34-0.98, p=0.041)
#> 2   age.factor60+ years 0.81 (0.48-1.36, p=0.426)
#> 3        sex.factorMale 0.98 (0.75-1.28, p=0.902)
#> 4    obstruct.factorYes 1.25 (0.90-1.76, p=0.186)
#> 5      perfor.factorYes 1.12 (0.51-2.44, p=0.770)