updates

man/jm.Rd

@@ -180,6 +180,12 @@ and \code{"backward"}.}
 
 \details{
 The mathematical details regarding the definition of the multivariate joint model, and the capabilities of the package can be found in the vignette in the doc directory.
+
+Notes:
+\itemize{
+\item{The ordering of the subjects in the datasets used to fit the mixed and Cox regression models needs to be the same.}
+\item{The units of the time variables in the mixed and Cox models need to be the same.}
+}
 }
 
 \value{

vignettes/JMbayes2.Rmd

@@ -52,6 +52,12 @@ and the density plot with the call:
 ggdensityplot(jointFit1, "alphas")
 ```
 
+### Notes
+
+- The ordering of the subjects in the datasets used to fit the mixed and Cox regression models needs to be the same.
+
+- The units of the time variables in the mixed and Cox models need to be the same.
+
 ## Multivariate
 To fit a joint model with multiple longitudinal outcomes, we simply provide a list of mixed models as the second argument of `jm()`. In the following example, we extend the joint model we fitted above by also including the prothrombin time and the log odds of the presence or absence of ascites as time-varying covariates in the relative risk model for the composite event. Ascites is a dichotomous outcome, and therefore, we fit a mixed-effects logistic regression model for it using the `mixed_model()` function from the **GLMMadaptive** package. The use of `||` in the `random` argument of `mixed_model()` specifies that the random intercepts and random slopes are assumed uncorrelated. In addition, the argument `which_independent` can be used to specify which longitudinal outcomes are to be assumed independent; here, as an illustration, we specify that the first (i.e., serum bilirubin) and second (i.e., prothrombin time) longitudinal outcomes are independent. To assume that all longitudinal outcomes are independent, we can use `jm(..., which_independent = "all")`. Because this joint model is more complex, we increase the number of MCMC iterations, the number of burn-in iterations, and the thinning per chain using the corresponding control arguments: 
 ```{r}