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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