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Comparison with other models

This vignette shows a brief comparison of the survival_ln_mixture with other survival models, available through censored.

We begin by loading the packages and preparing the data.

library(lnmixsurv)
library(readr)
require(censored)
require(purrr)
require(dplyr)
require(ggplot2)

set.seed(4)

# Gerando dados
data <- simulate_data(n = 6000, k = 3, mixture_components = 2, 
                      percentage_censored = 0.3)$data |> 
  filter(t < 500) |> 
  rename(x = cat, y = t) 

new_data <- data |>
  distinct(x)

formula <- Surv(y, delta) ~ x

For comparison, lets also estimate the Kaplan-Meier survival function.

library(ggsurvfit)

km <- survfit2(formula, data)

surv_km <- tidy_survfit(km, type = "surv") |>
  select(.eval_time = time, .pred_survival = estimate, id = strata) |>
  tidyr::nest(.pred = c(.eval_time, .pred_survival))

The we build our parsnip specifications and store them in a list.

ln_survival <- survival_reg(dist = "lognormal") |>
  set_engine("survival")

ph_survival <- proportional_hazards() |>
  set_engine("survival")

decision_tree <- decision_tree(cost_complexity = 0) |>
  set_engine("rpart") |>
  set_mode("censored regression")

ln_mixture <- survival_reg() |>
  set_engine("survival_ln_mixture",
             iter = 4000, warmup = 2000, starting_seed = 10, 
             em_iter = 450, mixture_components = 3)

ln_mixture_em <- survival_reg() |>
  set_engine("survival_ln_mixture_em",
             iter = 250, starting_seed = 15, 
             mixture_components = 3)

specs <- list(
  ln_survival = ln_survival, ph_survival = ph_survival, ln_mixture = ln_mixture, decision_tree = decision_tree, ln_mixture_em = ln_mixture_em
)

Finally, thanks to the great parsnip API, we can fit and predict all models at once.

set.seed(1)

models <- map(specs, ~ fit(.x, formula, data))

pred_sob <- map(models, ~ predict(.x, new_data,
                                  type = "survival",
                                  eval_time = seq(500)))

The following plot compares each model with the Kaplan-Meier estimates of the survival function.

all_preds <- bind_rows(pred_sob, .id = "modelo") |>
  group_by(modelo) |>
  dplyr::mutate(id = new_data$x) |>
  ungroup() |>
  tidyr::unnest(cols = .pred)

km_fit <- surv_km |>
  tidyr::unnest(cols = .pred) |>
  filter(.eval_time < 500)

ggplot(aes(x = .eval_time, y = .pred_survival, col = id), data = all_preds) +
  theme_bw() +
  geom_line() +
  facet_wrap(~modelo) +
  geom_line(data = km_fit, linetype = "dashed")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.