censored provides engines for survival models from the parsnip package. The models include parametric survival models, proportional hazards models, decision trees, boosted trees, bagged trees, and random forests. See the "Fitting and Predicting with censored" article for various examples. See below for examples of classic survival models and how to fit them with censored.
Author
Maintainer: Hannah Frick hannah@posit.co (ORCID)
Authors:
Emil Hvitfeldt emil.hvitfeldt@posit.co (ORCID)
Other contributors:
Posit Software, PBC [copyright holder, funder]
Examples
# Accelerated Failure Time (AFT) model
fit_aft <- survival_reg(dist = "weibull") %>%
set_engine("survival") %>%
fit(Surv(time, status) ~ age + sex + ph.karno, data = lung)
predict(fit_aft, lung[1:3, ], type = "time")
#> # A tibble: 3 × 1
#> .pred_time
#> <dbl>
#> 1 355.
#> 2 374.
#> 3 416.
# Cox's Proportional Hazards model
fit_cox <- proportional_hazards() %>%
set_engine("survival") %>%
fit(Surv(time, status) ~ age + sex + ph.karno, data = lung)
predict(fit_cox, lung[1:3, ], type = "time")
#> # A tibble: 3 × 1
#> .pred_time
#> <dbl>
#> 1 325.
#> 2 343.
#> 3 379.
# Andersen-Gill model for recurring events
fit_ag <- proportional_hazards() %>%
set_engine("survival") %>%
fit(Surv(tstart, tstop, status) ~ treat + inherit + age + strata(hos.cat),
data = cgd
)
predict(fit_ag, cgd[1:3, ], type = "time")
#> # A tibble: 3 × 1
#> .pred_time
#> <dbl>
#> 1 319.
#> 2 319.
#> 3 319.