Improving fitting and predictions for flexible parametric survival models

Flexible parametric survival models have been available in Stata since 2000 with Patrick Royston’s stpm command. I developed stpm2 in 2008 which added various extensions. However, the command is old and does not take advantage of some of the features Stata has added over the years. I will introduce stpm3, which has been completely rewritten adds a number of useful features including, Full support for factor variables (including for time-dependent effects). Use of extended functions within a varlist. Incorporate various functions (splines, fractional polynomial functions, etc.) directly within a varlist. These also work when including interactions and time-dependent effects. Easier and more intuitive predictions. These full synchronize with the extended functions making predictions for complex models with multiple interactions/non-linear effects incredibly simple. Make predictions for specific covariate patterns and perform various types of contrasts. 8 Directly save predictions to one or more frames. This separates the data used to analyse the data and that used for predictions. Obtain various marginal estimates using standsurv. This synchronizes with stpm3 factor variables and extended functions making marginal estimates much easier and less prone to user mistakes for complex models Model on the log(hazard) scale. vii. Do all the above for standard survival models, competing risk models, multistate models and relative survival models all within the same framework.