This package includes functions for parametrically estimating the mean number of events in the presence of competing events. Researchers often disregard subsequent events in the competing risk setting although the event of interest might be of recurrent nature, e.g. heart attacks, strokes, complications etc.. One estimate of interest in this situation is the mean number of events in the presence of competing events, which can be estimated using the functions provided in this package.
The estimation of the mean number of events requires two steps:
JointFPM().predict()
in order to estimate the mean number of events.Please note that this package is currently under development and might change throughout the process.
For installing the package from CRAN please use
install.packages("JointFPM")If you would like to use the latest development version from GitHub please use
remotes::install_github("entjos/JointFPM")We will use a dataset of bladder cancer recurrences for the following
example. The dataset is included in the {survival} package
and includes information on bladder cancer patients receiving three
different treatments: placebo, Pyridoxine, and Thiotepa
(cf. help(survival::bladder1)).In order to fit a joint FPM
we first need to reshape the dataset into a stacked format, i.e. each
observation needs to have one row for the competing event and possible
multiple rows for the recurrent event. In the example below we use the
{data.table} package for the data preparation.
# Load packages
library(JointFPM)
library(data.table) # For data preparations
# Load bladder cancer dataset from survival package
bldr_df <- as.data.table(survival::bladder1)
bldr_df <- bldr_df[, .(id, treatment, start, stop, status)]
# Define dataset for competing event times
bldr_ce <- bldr_df[, .SD[stop == max(stop)],
by = id]
bldr_ce[, `:=`(ce = 1,
re = 0,
event = as.numeric(status %in% 2:3),
start = 0)]
# Define dataset for bladder cancer recurrences
bldr_re <- bldr_df[,
`:=`(ce = 0,
re = 1,
event = as.numeric(status == 1))]
# Combine datasets into one stacked dataset
bldr_stacked <- rbindlist(list(bldr_ce, bldr_re))
bldr_stacked[, `:=`(pyridoxine = as.numeric(treatment == "pyridoxine"),
thiotepa = as.numeric(treatment == "thiotepa"))]
bldr_stacked$stop[bldr_stacked$stop == 0] <- 1 # Add one day survival
# Print stacked dataset
head(bldr_stacked) id treatment start stop status ce re event pyridoxine thiotepa
1: 1 placebo 0 1 3 1 0 1 0 0
2: 2 placebo 0 1 3 1 0 1 0 0
3: 3 placebo 0 4 0 1 0 0 0 0
4: 4 placebo 0 7 0 1 0 0 0 0
5: 5 placebo 0 10 3 1 0 1 0 0
6: 6 placebo 0 10 3 1 0 1 0 0The next step is to fit a joint flexible parametric model using the stacked dataset.
bldr_model <- JointFPM(Surv(time = start,
time2 = stop,
event = event,
type = 'counting') ~ 1,
re_model = ~ pyridoxine + thiotepa,
ce_model = ~ pyridoxine + thiotepa,
re_indicator = "re",
ce_indicator = "ce",
df_ce = 3,
df_re = 3,
tvc_ce_terms = list(pyridoxine = 2,
thiotepa = 2),
tvc_re_terms = list(pyridoxine = 2,
thiotepa = 2),
cluster = "id",
data = bldr_stacked)Based on the model we can predict the mean number of events at different time points and covariate patterns. Please note the estimation of confidence intervals for the mean number of events is computer intensive. The following code might take some minutes to run on your machine.
predict(bldr_model,
newdata = data.frame(pyridoxine = 1,
thiotepa = 0),
t = c(10, 20, 50)) stop fit lci uci
1 10 0.6068452 0.2866913 0.9269992
2 20 1.1157394 0.5335082 1.6979705
3 50 2.3793386 1.0346551 3.7240220If you find any bugs or have any suggestion please don’t hesitate to file an issue on GitHub.