Simple and complex survival analysis: New developments in merlin

At previous Stata conferences, I've presented survsim for simulating survival data, multistate for multistate parametric survival analysis, and merlin for fitting general mixed-effects regression models for linear, nonlinear, and user-defined distributions. In this talk, I'll present some ongoing work that brings together the codebase of all three commands into one coherent framework. This will provide new features such as simulating survival times from any survival model fit using merlin, allowing merlin to be used as a transition model in a multistate survival analysis—which enables, for example, the modeling of multiple timescales—and incorporating interval-censoring into standard and flexible parametric survival and cause-specific competing risks models, directly within merlin. To summarize, merlin can incorporate anything from the simplest parametric proportional hazards models to complex, nonlinear, hierarchical survival models. Possibilities are endless in terms of accounting for many challenges arising in clinical applications.