Output from msm, representing a fitted
multi-state model object.
from
State from which to consider survival. Defaults to
state 1.
to
Absorbing state to consider. Defaults to the
highest-labelled 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 "mean", denoting the means of the covariates in
the data (this is the default),
the number 0, indicating that all the covariates should be
set to zero,
or a list of values, with optional names. For example
list (60, 1)
where the order of the list follows the order of the covariates
originally given in the model formula, or a named list,
list (age = 60, sex = 1)
ci
If "none" (the default) no confidence intervals are
plotted. If "normal" or "bootstrap", confidence
intervals are plotted based on the respective method in
pmatrix.msm. This is very computationally-intensive,
since intervals must be computed at a series of times.
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 interp="start" (the default) then the entry
time into the absorbing state is assumed to be the time it is first
observed in the data.
If interp="midpoint" then the entry time into the absorbing
state is assumed to be halfway between the time it is first observed
and the previous observation time. This is generally more reasonable
for "progressive" models with observations at arbitrary times.
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 par.
lwd
Line width for the fitted curve. See par.
col
Colour for the fitted curve. See par.
lty.ci
Line type for the fitted curve confidence limits. See par.
lwd.ci
Line width for the fitted curve confidence limits. See par.
col.ci
Colour for the fitted curve confidence limits. See par.
mark.time
Mark the empirical survival curve at each censoring
point, see lines.survfit.
col.surv
Colour for the empirical survival curve, passed to lines.survfit. See par.
lty.surv
Line type for the empirical survival curve, passed to lines.survfit. See par.
lwd.surv
Line width for the empirical survival curve, passed to lines.survfit. See par.
...
Other arguments to be passed to the
plot function which draws the fitted curve, or the lines.survfit
function which draws the empirical curve.
Details
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 Kaplan-Meier estimate of the survival probability will
be an overestimate.
The method of Turnbull (1976) could be used to give a non-parametric
estimate of the time to an interval-censored event, and compared to
the equivalent estimate from a multi-state model. This is implemented
in the CRAN package interval (Fay and Shaw 2010).
This currently only handles time-homogeneous models.
References
Turnbull, B. W. (1976) The empirical distribution function with arbitrarily grouped, censored and
truncated data. J. R. Statist. Soc. B 38, 290-295.
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):1-34.