  When constructing models to summarize clinical data to be used for
simulations, it is good practice to evaluate the models for their capacity to
reproduce the data. This can be done by means of Visual Predictive Checks
(VPC), which consist of (1) several reproductions of the original study by
simulation from the model under evaluation, (2) calculating estimates of
interest for each simulated study and (3) comparing the distribution of those
estimates with the estimate from the original study. This procedure is a
generic method that is straightforward to apply, in general. Here we consider
the application of the method to time to event data and consider the special
case when a time-varying covariate is not known or cannot be approximated after
event time. In this case, simulations cannot be conducted beyond the end of the
follow-up time (event or censoring time) in the original study. Thus, the
simulations must be censored at the end of the follow-up time. Since this
censoring is not random, the standard KM estimates from the simulated studies
and the resulting VPC will be biased. We propose to use inverse probability of
censoring weighting (IPoC) method to correct the KM estimator for the simulated
studies and obtain unbiased VPCs. For analyzing the Cantos study, the IPoC
weighting as described here proved valuable and enabled the generation of VPCs
to qualify PKPD models for simulations. Here, we use a generated data set,
which allows illustration of the different situations and evaluation against
the known truth.
