Description Usage Arguments Details Value Author(s) See Also
Compute the estimate and approximate standard error of the ratio of two estimated transition intensities from a fitted multistate model at a given set of covariate values.
1 2 3  qratio.msm(x, ind1, ind2, covariates = "mean",
ci=c("delta","normal","bootstrap","none"), cl = 0.95,
B=1000, cores=NULL)

x 
A fitted multistate model, as returned by

ind1 
Pair of numbers giving the indices in the intensity matrix
of the numerator of the ratio, for example, 
ind2 
Pair of numbers giving the indices in the intensity matrix
of the denominator of the ratio, for example, 
covariates 
The covariate values at which to estimate the intensities.
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 to present. Defaults to 0.95. 
B 
Number of bootstrap replicates, or number of normal simulations from the distribution of the MLEs 
cores 
Number of cores to use for bootstrapping using parallel
processing. See 
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.
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.
C. H. Jackson [email protected]
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