Binomial logistic regression multivariable models with bootstrapped confidence intervals: finalfit model wrapper

Source: R/glmmultiboot.R

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

glmmulti_boot(.data, dependent, explanatory, R = 1000)

Arguments

.data

Dataframe.

dependent

Character vector length 1: name of depdendent variable (must have 2 levels).

explanatory

Character vector of any length: name(s) of explanatory variables.

R

Number of draws.

Value

A multivariable glm fitted model with bootstrapped confidence intervals. Output is of class glmboot.

Details

Uses glm with finalfit modelling conventions. boot::boot is used to draw bootstrapped confidence intervals on fixed effect model coefficients. Output can be passed to fit2df.

See also

fit2df, finalfit_merge

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

Examples

library(finalfit)
library(dplyr)
## Note number of draws set to 100 just for speed in this example
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"

colon_s %>%
  glmmulti_boot(dependent, explanatory, R=100) %>%
  fit2df(estimate_suffix="(multivariable (BS CIs))")
#>             explanatory OR(multivariable (BS CIs))
#> 1 age.factor40-59 years  0.57 (0.30-0.96, p=0.020)
#> 2   age.factor60+ years  0.81 (0.39-1.39, p=0.500)
#> 3        sex.factorMale  0.98 (0.75-1.36, p=0.740)
#> 4    obstruct.factorYes  1.25 (0.76-1.85, p=0.200)
#> 5      perfor.factorYes  1.12 (0.44-2.10, p=0.680)