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.
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A named list of
A data frame of prediction points and covariates. Must represent midpoints of equidistant intervals.
Numeric, the length of the intervals. Defaults to the
differences in the first column of
Scalar. The number of simulations, that is samples from the (posterior) distribution of the model parameters.
Numerical vector of length
numeric. 1 minus the confidence level used in calculating the c.i.s
Character. What simulation samples should be
Three-way array of simulated cumulative risks classified by
1) time points, 2) causes (incl. surv) and 3) Samples. A structure as
A named list of three-way arrays with results from simulation
(parametric bootstrap) from the distribution of the parameters in the
Crisk Cumulative risks for the
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
nrow(nd)+1, except for
where it is only
0" not included.
Stime has values
Surv plus the names of the list
Srisk has length
length(mod), with each
level representing a cumultive sum of cumulatieve risks, in order
indicated by the
quantiles of the quantities derived from the bootstrap
alpha is different from 0.05, names are of
Bendix Carstensen, http://bendixcarstensen.com
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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)
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