Using finalfit conventions, produces univariable Competing Risks Regression models for a set of explanatory variables.

crruni(.data, dependent, explanatory, ...)

Arguments

.data

Data frame or tibble.

dependent

Character vector of length 1: name of survival object in form Surv(time, status). Status default values should be 0 censored (e.g. alive), 1 event of interest (e.g. died of disease of interest), 2 competing event (e.g. died of other cause).

explanatory

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

...

Other arguments to crr

Value

A list of univariable crr fitted models class crrlist.

Details

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

See also

Examples

library(dplyr) melanoma = boot::melanoma melanoma = melanoma %>% mutate( # Cox PH to determine cause-specific hazards status_coxph = ifelse(status == 2, 0, # "still alive" ifelse(status == 1, 1, # "died of melanoma" 0)), # "died of other causes is censored" # Fine and Gray to determine subdistribution hazards status_crr = ifelse(status == 2, 0, # "still alive" ifelse(status == 1, 1, # "died of melanoma" 2)), # "died of other causes" sex = factor(sex), ulcer = factor(ulcer) ) dependent_coxph = c("Surv(time, status_coxph)") dependent_crr = c("Surv(time, status_crr)") explanatory = c("sex", "age", "ulcer") # Create single well-formatted table melanoma %>% summary_factorlist(dependent_crr, explanatory, column = TRUE, fit_id = TRUE) %>% ff_merge( melanoma %>% coxphmulti(dependent_coxph, explanatory) %>% fit2df(estimate_suffix = " (Cox PH multivariable)") ) %>% ff_merge( melanoma %>% crrmulti(dependent_crr, explanatory) %>% fit2df(estimate_suffix = " (competing risks multivariable)") ) %>% select(-fit_id, -index) %>% dependent_label(melanoma, dependent_crr)
#> Dependent variable is a survival object
#> Dependent: Surv(time, status_crr) all #> 2 sex 0 126 (61.5) #> 3 1 79 (38.5) #> 1 age Mean (SD) 52.5 (16.7) #> 4 ulcer 0 115 (56.1) #> 5 1 90 (43.9) #> HR (Cox PH multivariable) HR (competing risks multivariable) #> 2 - - #> 3 1.60 (0.95-2.71, p=0.080) 1.61 (0.94-2.75, p=0.084) #> 1 1.01 (1.00-1.03, p=0.107) 1.01 (0.99-1.03, p=0.370) #> 4 - - #> 5 4.02 (2.25-7.21, p<0.001) 3.81 (2.16-6.72, p<0.001)