Compute cumulative risk and/or expected sojourn time from competing risks models.
Consider a list of parametric models for rates of competing events, such as different causes of death, A, B, C, say. From estimates of the cause-specific rates we can then by simple numerical integration compute the cumulative risk of being in each state ('Surv' (=no event) and A, B and C) at different times, as well as the stacked cumulative rates such as A, A+C, A+C+Surv. Finally, we can compute the expected (truncated) sojourn times in each state up to each time point.
This function does this for simulated samples from the parameter vectors of supplied model objects, and computes the mentioend quantities with simulation-based confidence intervals. Some call this a prametric bootstrap.
The times and other covariates determining the cause-specific rates must be supplied in a data frame which will be used for predicting rates for all transitions.
ci.Crisk(mods,
nd,
int = mean(diff(nd[, 1])),
nB = 1000,
perm = length(mods):0 + 1,
alpha = 0.05,
sim.res = 'none')
sim2ci.Crisk(probs, alpha = 0.05)
sim2ci.Srisk(probs, perm = 1:dim(probs)[2],
alpha = 0.05)
sim2ci.Stime(probs, int = attr(probs, "int"),
alpha = 0.05)mods |
A named list of |
nd |
A data frame of prediction points and covariates. Must represent midpoints of equidistant intervals. |
int |
Numeric, the length of the intervals. Defaults to the
differences in the first column of |
nB |
Scalar. The number of simulations, that is samples from the (posterior) distribution of the model parameters. |
perm |
Numerical vector of length |
alpha |
numeric. 1 minus the confidence level used in calculating the c.i.s |
sim.res |
Character. What simulation samples should be
returned. If |
probs |
Three-way array of simulated cumulative risks classified by
1) time points, 2) causes (incl. surv) and 3) Samples. A structure as
returned by |
A named list of three-way arrays with results from simulation
(parametric bootstrap) from the distribution of the parameters in the
models in mods:
Crisk Cumulative risks for the length(mods)
events and the survival
Srisk Stacked versions of the cumulative risks
Stime Sojourn times in each states
All three arrays have (almost) the same dimensions:
time: end points of intervals starting with
"0". Length nrow(nd)+1, except for Stime
where it is only nrow(nd), "0" not included.
State. Crisk and Stime has values
Surv plus the names of the list mods (first
argument). Srisk has length length(mod), with each
level representing a cumultive sum of cumulatieve risks, in order
indicated by the perm argument.
ci.50%, ci.2.5%, ci.97.5% representing
quantiles of the quantities derived from the bootstrap
samples. If alpha is different from 0.05, names are of
course different.
Bendix Carstensen, http://bendixcarstensen.com
library(Epi)
data(DMlate)
# A Lexis object for survival
Ldm <- Lexis(entry = list( per = dodm,
age = dodm-dobth,
tfd = 0 ),
exit = list( per = dox ),
exit.status = factor( !is.na(dodth), labels = c("DM","Dead") ),
data = DMlate[sample(1:nrow(DMlate),1000),] )
summary(Ldm, timeScales = TRUE)
# Cut at OAD and Ins times
Mdm <- mcutLexis( Ldm,
wh = c('dooad','doins'),
new.states = c('OAD','Ins'),
precursor = 'Alive',
seq.states = FALSE,
ties = TRUE )
summary( Mdm$lex.dur )
# restrict to DM state
Sdm <- splitLexis(factorize(subset(Mdm, lex.Cst == "DM")),
time.scale = "tfd",
breaks = seq(0,20,1/12))
summary(Sdm)
summary(Relevel(Sdm, c(1, 4, 2, 3)))
boxes(Relevel(Sdm, c(1, 4, 2, 3)),
boxpos = list(x = c(15, 85, 80, 15),
y = c(85, 85, 20, 15)),
scale.R = 100)
# glm models for the cause-specific rates
system.time(
mD <- glm.Lexis(Sdm, ~ Ns(tfd, knots=0:6*2), to = 'Dead') )
system.time(
mO <- glm.Lexis(Sdm, ~ Ns(tfd, knots=0:6*2), to = 'OAD' ) )
system.time(
mI <- glm.Lexis(Sdm, ~ Ns(tfd, knots=0:6*2), to = 'Ins' ) )
# intervals for calculation of predicted rates
int <- 1/100
nd <- data.frame( tfd = seq(int,10,int)-int/2 ) # not the same as the split,
# and totally unrelated to it
# cumulaive risks with confidence intervals
# (too few timepoints, too few simluations)
system.time(
res <- ci.Crisk(list(OAD = mO,
Ins = mI,
Dead = mD),
nd = data.frame(tfd = (1:100-0.5)/10),
nB = 100,
perm = 4:1))
str(res)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.