The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance. Due to the lack of information content under conditions where very few events were observed, all three methods exhibit parameter bias and imprecision, however most pronounced by the Laplace method. The method performance was assessed by parameter bias and precision. Various conditions were investigated, ranging from rare to frequent events and from low to high interindividual variability.

A stochastic simulation and estimation study was performed to assess the performance of the three estimation methods when applied to a repeated time-to-event model with a constant hazard associated with an exponential interindividual variability. The Laplace method already existed, whereas the two latter methods have recently become available in NONMEM 7. The aim of this investigation was to assess the performance of the Laplace method, the stochastic approximation expectation-maximization (SAEM) method, and the importance sampling method when modeling repeated time-to-event data. Resampling procedures such as bootstrapping, sampling importance resampling 14 and randomization. To construct the full covariate model, all the 10 candidate covariates were included in the model on CL/F at the same time. For illustration purpose, we only evaluate the covariate effects on CL/F. Event data is often low in information content and the mixed-effects modeling software NONMEM has previously been shown to perform poorly with low information ordered categorical data. The model fitting was performed in NONMEM. It is not uncommon that the outcome measurements, symptoms or side effects, of a clinical trial belong to the family of event type data, e.g., bleeding episodes or emesis events.

Nonmem importance sampling