Compute the estimate and approximate standard error of the ratio of two estimated transition intensities from a fitted multi-state model at a given set of covariate values.
qratio.msm(x, ind1, ind2, covariates = "mean",
ci=c("delta","normal","bootstrap","none"), cl = 0.95,
B=1000, cores=NULL)A fitted multi-state model, as returned by
msm.
Pair of numbers giving the indices in the intensity matrix
of the numerator of the ratio, for example, c(1,2).
Pair of numbers giving the indices in the intensity matrix
of the denominator of the ratio, for example, c(2,1).
The covariate values at which to estimate the intensities. 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)
If "delta" (the default) then confidence intervals are
calculated by the delta method.
If "normal", then calculate a confidence interval by
simulating B random vectors
from the asymptotic multivariate normal distribution implied by the
maximum likelihood estimates (and covariance matrix) of the log
transition intensities and covariate effects, then transforming.
If "bootstrap" then calculate a confidence interval by
non-parametric bootstrap refitting. This is 1-2 orders of magnitude
slower than the "normal" method, but is expected to be more
accurate. See boot.msm for more details of
bootstrapping in msm.
Width of the symmetric confidence interval to present. Defaults to 0.95.
Number of bootstrap replicates, or number of normal simulations from the distribution of the MLEs
Number of cores to use for bootstrapping using parallel
processing. See boot.msm for more details.
A named vector with elements estimate, se, L and U
containing the estimate, standard error, lower and upper confidence
limits, respectively, of the ratio of intensities.
For example, we might want to compute the ratio of the progression
rate and recovery rate for a fitted model disease.msm with a
health state (state 1) and a disease state
(state 2). In this case, the progression rate is the (1,2) entry of
the intensity matrix, and the recovery rate is the (2,1) entry.
Thus to compute this ratio with covariates set to their means, we
call
qratio.msm(disease.msm, c(1,2), c(2,1)) .
Standard errors are estimated by the delta method. Confidence limits are estimated by assuming normality on the log scale.