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

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 (0.35-0.91, p<0.001)
#> 2   age.factor60+ years  0.81 (0.44-1.31, p=0.520)
#> 3        sex.factorMale  0.98 (0.73-1.32, p=0.860)
#> 4    obstruct.factorYes  1.25 (0.88-1.78, p=0.080)
#> 5      perfor.factorYes  1.12 (0.35-2.69, p=0.760)