crruni | R Documentation |
finalfit
model wrapperUsing finalfit
conventions, produces univariable Competing Risks
Regression models for a set of explanatory variables.
crruni(.data, dependent, explanatory, ...)
.data |
Data frame or tibble. |
dependent |
Character vector of length 1: name of survival object in
form |
explanatory |
Character vector of any length: name(s) of explanatory variables. |
... |
Other arguments to |
Uses crr
with finalfit
modelling conventions.
Output can be passed to fit2df
.
A list of univariable crr
fitted models class
crrlist
.
fit2df, finalfit_merge
Other finalfit model wrappers:
coxphmulti()
,
coxphuni()
,
crrmulti()
,
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)
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