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

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 (0.25-1.03, p=0.060) #> 2 age.factor60+ years 0.81 (0.41-1.35, p=0.580) #> 3 sex.factorMale 0.98 (0.73-1.17, p=0.580) #> 4 obstruct.factorYes 1.25 (0.92-1.80, p=0.140) #> 5 perfor.factorYes 1.12 (0.54-2.82, p=0.720)