mstate (version 0.3.3)

msfit.relsurv: Extend a multi-state model using relative survival

Description

A function that takes a fitted msfit object and upgrades it using relative survival, where chosen transitions are split in population and excess transitions. This upgraded msfit object contains the split hazards based on the transition matrix (transMat). The (co)variance matrix is also upgraded, if provided.

Usage

msfit.relsurv(
  msfit.obj,
  data,
  split.transitions,
  ratetable = relsurv::slopop,
  rmap,
  time.format = "days",
  var.pop.haz = c("fixed", "bootstrap", "both"),
  B = 10,
  seed = NULL,
  add.times,
  substitution = TRUE,
  link_trans_ind = FALSE
)

Value

Returns a msfit object that contains estimates for the extended model with split (population and excess) transitions.

Arguments

msfit.obj

The msfit object which has to be upgraded

data

The data used for fitting the msfit model

split.transitions

An integer vector containing the numbered transitions that should be split. Use same numbering as in the given transition matrix

ratetable

The population mortality table. A table of event rates, organized as a ratetable object, see for example relsurv::slopop. Default is slopop

rmap

An optional list to be used if the variables in the data are not organized (and named) in the same way as in the ratetable object

time.format

Define the time format which is used in the data. Possible options: c('days', 'years', 'months'). Default is 'days'

var.pop.haz

If 'fixed' (default), the Greenwood estimator for the variances is used, where it is assumed that the variance of the population hazards is zero. If 'bootstrap', one gets boostrap estimates for all all transitions. Option 'both' gives both variance estimates

B

Number of bootstrap replications. Relevant only if var.pop.haz == 'bootstrap' or 'both'. Default is B=10.

seed

Set seed

add.times

Additional times at which hazards should be evaluated

substitution

Whether function substitute should be used for rmap argument. Default is TRUE

link_trans_ind

Choose whether the linkage between the old and new transition matrix should be saved. Default is FALSE.

Author

Damjan Manevski

References

Manevski D, Putter H, Pohar Perme M, Bonneville EF, Schetelig J, de Wreede LC (2021). Integrating relative survival in multi-state models -- a non-parametric approach. https://arxiv.org/abs/2106.12399

See Also

msfit

Examples

if (FALSE) {
library(mstate)
# Load dataset:
data("ebmt1")
# Transition matrix:
tmat <- transMat(list(c(2,3),c(4), c(), c()), 
                 names = c("Alive relapse-free", "Relapse","NRM", "DaR"))
# Data in long format using msprep
df <- msprep(time=c(NA,"rel","srv","srv"), status=c(NA,"relstat","srvstat","srvstat"),
             data=ebmt1, trans=tmat)
# Generate demographic covariates (which are usually present in datasets) 
# and based on them estimate the population hazard.
set.seed(510)
df$age <- runif(nrow(df), 45, 65)
df$sex <- sample(c("male", "female"), size = nrow(df), replace = TRUE)
df$dateHCT <- sample(seq(as.Date('1990/01/01'), 
    as.Date('2000/01/01'), by="day"), nrow(df), replace = TRUE) # generate years
# Cox object:
cx <- coxph(Surv(Tstart,Tstop,status)~strata(trans),
            data=df,method="breslow")
# Basic multi-state model:
mod <- msfit(cx,trans=tmat)
# Extended multi-state model, where the two transition
# reaching death are split in excess and population parts.
# We assume patients live like in the Slovene population,
# thus we use Slovene mortality tables in this example.
# Variances estimated using 25 bootstrap replications.
mod.relsurv <- msfit.relsurv(msfit.obj = mod, data=df, split.transitions = c(2,3),
                            ratetable = relsurv::slopop, 
                            rmap = list(age=age*365.241, year=dateHCT),
                            time.format = "days",
                            var.pop.haz = "bootstrap",
                            B = 25)
# Estimate transition probabilities:
pt <- probtrans(mod.relsurv, predt=0, method='greenwood')
# Estimated cumulative hazards with the corresponding 
# bootstrap standard errors at 300, 600, 900 days:
summary(object = mod.relsurv, times = c(300, 600, 900), conf.type = 'log')
# Estimated transition probabilities together with the corresponding 
# bootstrap standard errors and log.boot confidence intervals 
# at 300, 600, 900 days:
summary(object = pt, times = c(300, 600, 900), conf.type = 'log')
# Plot the measures:
plot(mod.relsurv, use.ggplot = TRUE)
plot(pt, use.ggplot = TRUE)
}