qratio.msm(x, ind1, ind2, covariates = "mean",
ci=c("delta","normal","bootstrap","none"), cl = 0.95, B=1000)msmc(1,2).c(2,1)."mean", denoting the means of the covariates in
the data (this is the default),
the number 0, indicating that all the covariates s"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 multivestimate, se, L and U
containing the estimate, standard error, lower and upper confidence
limits, respectively, of the ratio of intensities.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.
qmatrix.msm