Plot a KaplanMeier estimate of the survival probability and compare
it with the fitted survival probability from a msm
model.
1 2 3 4 5 6 7  ## S3 method for class 'survfit.msm'
plot(x, from=1, to=NULL, range=NULL, covariates="mean",
interp=c("start","midpoint"), ci=c("none","normal","bootstrap"), B=100,
legend.pos=NULL, xlab="Time", ylab="Survival probability",
lty=1, lwd=1, col="red", lty.ci=2, lwd.ci=1, col.ci="red",
mark.time=TRUE, col.surv="blue", lty.surv=2, lwd.surv=1,
...)

x 
Output from 
from 
State from which to consider survival. Defaults to state 1. 
to 
Absorbing state to consider. Defaults to the highestlabelled absorbing state. 
range 
Vector of two elements, giving the range of times to plot for. 
covariates 
Covariate values for which to evaluate the expected
probabilities. This can either be: the string the number or a list of values, with optional names. For example
where the order of the list follows the order of the covariates originally given in the model formula, or a named list,

ci 
If 
B 
Number of bootstrap or normal replicates for the confidence interval. The default is 100 rather than the usual 1000, since these plots are for rough diagnostic purposes. 
interp 
If If 
legend.pos 
Vector of the x and y position, respectively, of the legend. 
xlab 
x axis label. 
ylab 
y axis label. 
lty 
Line type for the fitted curve. See 
lwd 
Line width for the fitted curve. See 
col 
Colour for the fitted curve. See 
lty.ci 
Line type for the fitted curve confidence limits. See 
lwd.ci 
Line width for the fitted curve confidence limits. See 
col.ci 
Colour for the fitted curve confidence limits. See 
mark.time 
Mark the empirical survival curve at each censoring
point, see 
col.surv 
Colour for the empirical survival curve, passed to 
lty.surv 
Line type for the empirical survival curve, passed to 
lwd.surv 
Line width for the empirical survival curve, passed to 
... 
Other arguments to be passed to the

If the data represent observations of the process at arbitrary times, then the first occurrence of the absorbing state in the data will usually be greater than the actual first transition time to that state. Therefore the KaplanMeier estimate of the survival probability will be an overestimate.
The method of Turnbull (1976) could be used to give a nonparametric estimate of the time to an intervalcensored event, and compared to the equivalent estimate from a multistate model. This is implemented in the CRAN package interval (Fay and Shaw 2010).
This currently only handles timehomogeneous models.
Turnbull, B. W. (1976) The empirical distribution function with arbitrarily grouped, censored and truncated data. J. R. Statist. Soc. B 38, 290295.
Fay, MP and Shaw, PA (2010). Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R package. Journal of Statistical Software. http://www.jstatsoft.org/v36/ i02/. 36 (2):134.
survfit
, plot.survfit
, plot.prevalence.msm
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