totlos.msm {msm} | R Documentation |
Estimate the expected total length of stay in each set of states, for a given period of evolution of a multi-state model.
totlos.msm(x, start=1, end=NULL, fromt=0, tot=Inf, covariates="mean", ci=c("none","normal","bootstrap"), cl=0.95, B=1000, ...)
x |
A fitted multi-state model, as returned by
|
start |
State at the beginning of the period. |
end |
States to forecast the total length of stay in. Defaults to all states. |
fromt |
Time from which to estimate total length of stay. Defaults to 0, the beginning of the process. |
tot |
Time up to which total length of stay is estimated. Defaults
to infinity, giving the expected time spent in the state until
absorption. For models without an absorbing state, |
covariates |
The covariate values to estimate for. 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 If If |
cl |
Width of the symmetric confidence interval, relative to 1 |
B |
Number of bootstrap replicates |
... |
Further arguments to be passed to the
|
The expected total length of stay in state j between times
t_1 and t_2, from the point of view of an individual in
state i at time 0,
is defined by the integral from t_1 to
t_2 of the i,j entry of the transition probability matrix P(t).
As the individual entries of P(t) = exp(tQ)
are not available explicitly in terms of
t for a general Markov model, this integral is calculated
numerically, using the integrate
function. This may
take a long time for models with many states where P(t) is
expensive to calculate.
For a model where the individual has only one place to go from each state, and each state is visited only once, for example a progressive disease model with no recovery or death, these are equal to the mean sojourn time in each state. However, consider a three-state health-disease-death model with transitions from health to disease, health to death, and disease to death, where everybody starts healthy. In this case the mean sojourn time in the disease state will be greater than the expected length of stay in the disease state. This is because the mean sojourn time in a state is conditional on entering the state, whereas the expected total time diseased is a forecast for a healthy individual, who may die before getting the disease.
The only time-inhomogeneous models handled by this function are those
specified using the pci
option to msm
.
For any other inhomogeneous models, the function assumes that
covariates are constant on the forecasted interval.
A vector of expected total lengths of stay for each transient state.
C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk
sojourn.msm
, pmatrix.msm
, integrate
, boot.msm
.