The goal of netidmtpreg is to enable net survival estimation through direct binomial regression, allowing modeling continuous covariate effects that could not be handled through e.g stratified Pohar-Perme estimation, all this in a multistate Illness-Death setting.
You can install the development version of netidmtpreg like so:
devtools::install_github("qmarcou/netidmtpreg")If working from source through a git clone, the package should be
installed locally to get tests running future::multisession planning
working. This can be accomplished using devtools
devtools::dev_mode(on = TRUE)
devtools::install_local(force = TRUE) # force package update
devtools::load_all() # required to make tests visible
testthat::test_package("netidmtpreg")
# or testthat::test_check("netidmtpreg") to run R CMD checkThis is a basic example illustrating net survival estimation on data simulated using the package. Let’s first generate some data:
library(netidmtpreg)
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
n_ind <- 5e2 # number of simulated individuals
# Generate exponentially distributed event times without censoring
synth_idm_data <- generate_uncensored_ind_exp_idm_data(
n_individuals = n_ind,
lambda_illness = 1.0,
lambda_death = 1.0
)
# Generate random age and sex labels
synth_idm_data <-
synth_idm_data %>% tibble::add_column(
sex = ifelse(rbinom(n_ind, 1, prob = .5), "male", "female"),
age = runif(n = n_ind, min = 50, max = 80)
)
# Generate random start of follow up dates
synth_idm_data <-
synth_idm_data %>% tibble::add_column(start_date = as.Date.numeric(
x = rnorm(n = n_ind, mean = 0, sd = 1e2),
origin = as.Date("15/06/1976", "%d/%m/%Y")
))
# Generate population mortality assuming equal constant population rate
l_pop_death <- 1.0 # extra disease mortality doubles the population mortality
population_death_times <- generate_exponential_time_to_event(
n_individuals = n_ind,
lambda = l_pop_death
)
# create a corresponding ratetable object
const_ratetable <- survival::survexp.us
const_ratetable[] <- l_pop_death # ratetable's covariate do not matter
# Update death time accordingly to create an observed crude survival dataset
crude_synth_idm_data <- netidmtpreg:::apply_iddata_death(
synth_idm_data,
population_death_times
)Now let’s carry net survival estimation:
# Estimation can be sped up and carried in parrallel using futures:
future::plan("multisession") # will work on any OS
# future::plan("multicore") # more efficient but only works on UNIX systems
net_estimate <- fit_netTPreg(
formula = ~1, # intercept only model, similar to Pohar-Perme estimation
data = crude_synth_idm_data,
# Use a standard ratetable
ratetable = const_ratetable,
rmap = list(
age = age,
sex = sex,
year = start_date
),
time_dep_popvars = list("age", "year"),
s = 0.2,
by = n_ind / 10,
trans = "11",
link = "logit",
R = 100 # Number of bootstraps
)
#> [1] "estimate"
#> [1] "bootstrap"
future::plan("sequential") # close the multisession, see future's documentationWe obtain a TPreg object with a dedicated plotting method using
ggplot:
plot(net_estimate) + ggplot2::coord_cartesian(ylim = c(-5, 5), clip = "off")
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).
Warning: note the use of ggplot2::coord_cartesian with clip="off"
instead of ggplot2::ylim. The former acts as a “zoom” command, while
the latter acts as a filter on input data, and as such might prevent
display of large confidence intervals.
The obtained TPreg plots are composable ggplot objects that can be combined. Let’s use this to compare our results with: 1) what would have been obtained without background mortality (‘ground truth’), and 2) what would have been obtained on the same data without taking into account population mortality (‘crude estimate’).
# Estimation can be sped up and carried in parrallel using futures:
future::plan("multisession") # will work on any OS
# future::plan("multicore") # more efficient but only works on UNIX systems
crude_estimate <- fit_netTPreg(
formula = ~1, # intercept only model, similar to Pohar-Perme estimation
data = crude_synth_idm_data,
# Use a standard ratetable
ratetable = NULL,
s = 0.2,
by = n_ind / 10,
trans = "11",
link = "logit",
R = 100 # Number of bootstraps
)
#> [1] "estimate"
#> [1] "bootstrap"
truth_estimate <- fit_netTPreg(
formula = ~1, # intercept only model, similar to Pohar-Perme estimation
data = synth_idm_data, # data without added population mortality
# Use a standard ratetable
ratetable = NULL,
s = 0.2,
by = n_ind / 10,
trans = "11",
link = "logit",
R = 100 # Number of bootstraps
)
#> [1] "estimate"
#> [1] "bootstrap"
future::plan("sequential") # close the multisession, see future's documentationYou can use the reserved keyword model to set the model name and
automatically set the linetype accordingly.
autoplot(net_estimate, model = "Net estimate") +
autolayer(crude_estimate, model = "Crude estimate") +
autolayer(truth_estimate, model = "Ground Truth") +
ggplot2::coord_cartesian(ylim = c(-5, 5), xlim = c(0, 2), clip = "off")
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).
These automated plotting routines are designed for quick inspection of
the model. If you need more flexibility you can rely on tidymodels
broom style methods
tidy, augment (to be implemented) and glance (to be
implemented) to leverage ggplot’s flexibility.
More examples to come!