Using finalfit conventions, produces multivariable Competing Risks
Regression models for a set of explanatory variables.
crrmulti(.data, dependent, explanatory, ...)Data frame or tibble.
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).
Character vector of any length: name(s) of explanatory variables.
Other arguments to crr
A multivariable crr fitted model
class crr.
Other finalfit model wrappers:
coxphmulti(),
coxphuni(),
crruni(),
glmmixed(),
glmmulti_boot(),
glmmulti(),
glmuni(),
lmmixed(),
lmmulti(),
lmuni(),
svyglmmulti(),
svyglmuni()
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)