Using finalfit conventions, produces multiple univariable Cox Proportional Hazard regression models for a set of explanatory variables against a survival object.

coxphuni(.data, dependent, explanatory)

Arguments

.data

Dataframe.

dependent

Character vector of length 1: name of survival object in form Surv(time, status).

explanatory

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

Value

A list of univariable coxph fitted model outputs. Output is of class coxphlist.

Details

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

See also

Examples

# Cox Proportional Hazards univariable analysis. library(finalfit) library(dplyr) explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "Surv(time, status)" colon_s %>% coxphuni(dependent, explanatory) %>% fit2df()
#> explanatory HR #> 1 age.factor40-59 years 0.76 (0.53-1.09, p=0.132) #> 2 age.factor60+ years 0.93 (0.66-1.31, p=0.668) #> 3 sex.factorMale 1.01 (0.84-1.22, p=0.888) #> 4 obstruct.factorYes 1.29 (1.03-1.62, p=0.028) #> 5 perfor.factorYes 1.17 (0.70-1.95, p=0.556)