Epi (version 2.56)

simLexis: Simulate a Lexis object representing follow-up in a multistate model.

Description

Based on a (pre-)Lexis object representing persons at given states and times, and full specification of transition intensities between states in the form of models for the transition rates, this function simulates transition times and -types for persons and returns a Lexis object representing the simulated cohort. The simulation scheme accommodates multiple timescales, including time since entry into an intermediate state, and accepts fitted Poisson models, Cox-models or just a function as specification of rates.

Usage

simLexis( Tr, init,
           N = 1,
      lex.id,
     t.range = 20,
       n.int = 101,
    time.pts = seq(0,t.range,length.out=n.int) )
nState( obj, at, from, time.scale = 1 )
pState( nSt, perm = 1:ncol(nSt) )
# S3 method for pState
plot( x,
                     col = rainbow(ncol(x)),
                  border = "transparent",
                    xlab = "Time",
                    ylim = 0:1,
                    ylab = "Probability", ... )
# S3 method for pState
lines( x,
                      col = rainbow(ncol(x)),
                   border = "transparent", ... )

Value

simLexis returns a Lexis object representing the experience of a population starting as init followed through the states according to the transitions in Tr.

The function nState returns a table of persons classified by states at each of the times in at. Note that this function can easily produce meaningless results, for example if applied to a

Lexis object not created by simulation. If you apply it to a

Lexis object generated by simLexis, you must make sure that you start (from) the point where you started the simulation on the correct timescale, and you will get funny results if you try to tabulate beyond the censoring time for the simulation. The resulting object has class "table".

The result from using pState on the result from nState

has class c("pState","matrix").

Arguments

Tr

A named list of named lists. The names of the list are names of the transient states in the model. Each list element is again a named list. The names of the elements of this inner list are the names of the states reachable from the state with name equal to the list. Elements of the intter lists represent transitions. See details.

init

A (pre-)Lexis object representing the initial state of the persons whose trajectories through the multiple states we want to simulate. Must have attributes "time.scales" and "time.since" --- see details. Duplicate values of lex.id are not sensible and not accepted.

N

Numeric. How many persons should be simulated. N persons with covariate configuration of each row in init will be simulated. Either a scalar or a vector of length nrow(init).

lex.id

Vector of ids of the simulated persons. Useful when simulating in chunks.

t.range

Numerical scalar. The range of time over which to compute the cumulative rates when simulating. Simulted times beyond this will result in an obervation censored at t.range after entry.

n.int

Number of intervals to use when computing (cumulative) rates.

time.pts

Numerical vector of times since start. Cumulative rates for transitions are computed at these times after stater and entry state. Simulation is only done till time max(time.pts) after start, where persons are censored. Must start with 0.

obj

A Lexis object.

from

The point on the time scale time.scale from which we start counting.

time.scale

The timescale to which from refer.

at

Time points (after from) where the number of persons in each state is to be computed.

nSt

A table obtained by nState.

perm

A permutation of columns used before cumulating row-wise and taking percentages.

x

An object of class pState, e.g. created by pState.

col

Colors for filling the areas between curves.

border

Colors for outline of the areas between curves.

xlab

Label on x-axis

ylim

Limits on y-axis

ylab

Label on y-axis

...

Further arguments passed on to plot.

Author

Bendix Carstensen, http://bendixcarstensen.com.

Details

The simulation command simLexis is not defined as a method for Lexis objects, because the input is not a Lexis object, the Lexis-like object is merely representing a prevalent population and a specification of which variables that are timescales. The variables lex.dur and lex.Xst are ignored (and overwritten) if present. The core input is the list Tr giving the transitions.

The components of Tr represents the transition intensities between states. The transition from state A to B, say, is assumed stored in Tr$A$B. Thus names of the elements of Tr are names of transient states, and the names of the elements of each these are the names of states reachable from the corresponding transient state.

The transition intensities are assumed modelled by either a glm with Poisson family or a Cox-model. In both cases the timescale(s) in the model must be using the names fo the timescales in a Lexis object representng the follow-up in a cohort, and the risk time must be taken from the variable lex.dur --- see the example.

Alternatively, an element in Tr could be a function that from a data frame produces transition rates, or specifically cumulative transition rates over intervals of length lex.dur.

The pre-Lexis object init must contain values of all variables used in any of the objects in Tr, as well as all timescales - even those not used in the models. Moreover, the attributes time.scales and time.since must be present. The attribute time.since is a character vector of the same length as time.scales and an element has value "A" if the corresponding time scale is defined as "time since entry into state A", otherwise the value is "". If not present it will be set to a vector of ""s, which is only OK if no time scales are defined as time since entry to a state.

Note that the variables pre-Lexis object init must have the same mode and class as in the dataset used for fitting the models --- hence the indexing of rows by brackets in the assignment of values used in the example below - this way the variables have their attributes preserved; using init[,"var"] <- or init$var <- replaces the variable, whereas init[1:4,"var"] <- or init$var[1:4] <- replaces values only and prevents you from entering non-existing factor levels etc.

The function Lexis automatically generates an attribute time.since, and cutLexis updates it when new time scales are defined. Hence, the simplest way of defining a initial pre-Lexis object representing a current state of a (set of) persons to be followed through a multistate model is to take NULL rows of an existing Lexis object (normally the one used for estimation), and so ensuring that all relevant attributes and state levels are properly defined. See the example code.

The prevalence function nState computes the distribution of individuals in different states at prespecified times. Only sensible for a simulated Lexis object. The function pState takes a matrix as output by nState and computes the row-wise cumulative probabilities across states, and leaves an object of class pState, suitable for plotting.

See Also

Lexis, cutLexis, splitLexis

Examples

data(DMlate)
dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ),
               exit = list(Per=dox),
        exit.status = factor(!is.na(dodth),labels=c("DM","Dead")),
               data = DMlate[runif(nrow(DMlate))<0.1,] )
# Split follow-up at insulin, introduce a new timescale,
# and split non-precursor states
dmi <- cutLexis( dml, cut = dml$doins,
                      pre = "DM",
                new.state = "Ins",
                new.scale = "t.Ins",
             split.states = TRUE )
# Split the follow in 1-year intervals for modelling
Si <- splitLexis( dmi, 0:30/2, "DMdur" )
# Define knots
nk <- 4
( ai.kn <- with( subset(Si,lex.Xst=="Ins"),
                 quantile( Age+lex.dur, probs=(1:nk-0.5)/nk ) ) )
( ad.kn <- with( subset(Si,lex.Xst=="Dead"),
                 quantile( Age+lex.dur, probs=(1:nk-0.5)/nk ) ) )
( di.kn <- with( subset(Si,lex.Xst=="Ins"),
                 quantile( DMdur+lex.dur, probs=(1:nk-0.5)/nk ) ) )
( dd.kn <- with( subset(Si,lex.Xst=="Dead"),
                 quantile( DMdur+lex.dur, probs=(1:nk-0.5)/nk ) ) )
( td.kn <- with( subset(Si,lex.Xst=="Dead(Ins)"),
                 quantile( t.Ins+lex.dur, probs=(1:nk-0.5)/nk ) ) )

# Fit Poisson models to transition rates
library( splines )
DM.Ins <- glm( (lex.Xst=="Ins") ~ Ns( Age  , knots=ai.kn ) +
                                  Ns( DMdur, knots=di.kn ) +
                                  I(Per-2000) + sex,
               family=poisson, offset=log(lex.dur),
               data = subset(Si,lex.Cst=="DM") )
DM.Dead <- glm( (lex.Xst=="Dead") ~ Ns( Age  , knots=ad.kn ) +
                                    Ns( DMdur, knots=dd.kn ) +
                                    I(Per-2000) + sex,
               family=poisson, offset=log(lex.dur),
               data = subset(Si,lex.Cst=="DM") )
Ins.Dead <- glm( (lex.Xst=="Dead(Ins)") ~ Ns( Age  , knots=ad.kn ) +
                                          Ns( DMdur, knots=dd.kn ) +
                                          Ns( t.Ins, knots=td.kn ) +
                                          I(Per-2000) + sex,
               family=poisson, offset=log(lex.dur),
               data = subset(Si,lex.Cst=="Ins") )

# Stuff the models into an object representing the transitions
Tr <- list( "DM" = list( "Ins"       = DM.Ins,
                         "Dead"      = DM.Dead  ),
           "Ins" = list( "Dead(Ins)" = Ins.Dead ) )
lapply( Tr, names )

# Define an initial object - note the subsetting that ensures that
# all attributes are carried over
ini <- Si[1,1:9][-1,]
ini[1:2,"lex.Cst"] <- "DM"
ini[1:2,"Per"] <- 1995
ini[1:2,"Age"] <- 60
ini[1:2,"DMdur"] <- 5
ini[1:2,"sex"] <- c("M","F")
str(ini)

# Simulate 200 of each sex using the estimated models in Tr
simL <- simLexis( Tr, ini, time.pts=seq(0,11,0.5), N=200 )
summary( simL )

# Find the number of persons in each state at a set of times.
# Note that the times are shirter than the time-span simulated.
nSt <- nState( subset(simL,sex=="M"),
               at=seq(0,10,0.1), from=1995, time.scale="Per" )
nSt

# Show the cumulative prevalences in a different order than that of the
# state-level ordering and plot them using all defaults
pp <- pState( nSt, perm=c(1,2,4,3) )
head( pp )
plot( pp )

# A more useful set-up of the graph
clr <- c("orange2","forestgreen")
par( las=1 )
plot( pp, col=clr[c(2,1,1,2)] )
lines( as.numeric(rownames(pp)), pp[,2], lwd=2 )
mtext( "60 year old male, diagnosed 1995", side=3, line=2.5, adj=0 )
mtext( "Survival curve", side=3, line=1.5, adj=0 )
mtext( "DM, no insulin   DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] )
mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] )
axis( side=4 )

# Using a Cox-model for the mortality rates assuming the two mortality
# rates to be proportional:
# When we fit a Cox-model, lex.dur must be used in the Surv() function,
# and the I() constrction must be used when specifying intermediate
# states as covariates, since factors with levels not present in the
# data will create NAs in the parameter vector returned by coxph, which
# in return will crash the simulation machinery.
library( survival )
Cox.Dead <- coxph( Surv( DMdur, DMdur+lex.dur,
                         lex.Xst %in% c("Dead(Ins)","Dead")) ~
                   Ns( Age-DMdur, knots=ad.kn ) +
                   I(lex.Cst=="Ins") +
                   I(Per-2000) + sex,
               data = Si )
Cr <- list( "DM" = list( "Ins"       = DM.Ins,
                         "Dead"      = Cox.Dead  ),
           "Ins" = list( "Dead(Ins)" = Cox.Dead ) )
simL <- simLexis( Cr, ini, time.pts=seq(0,11,0.2), N=200 )
summary( simL )
nSt <- nState( subset(simL,sex=="M"),
               at=seq(0,10,0.2), from=1995, time.scale="Per" )
pp <- pState( nSt, perm=c(1,2,4,3) )
plot( pp )