Multistate survival analysis in Stata

Multistate models are increasingly being used to model complex disease profiles. By modeling transitions between disease states, accounting for competing events at each transition, we can gain a much richer understanding of patient trajectories and how risk factors impact over the entire disease pathway. In this talk, we will introduce some new Stata commands for the analysis of multistate survival data. This includes msset, a data preparation tool that converts a dataset from wide (one observation per subject, multiple time and status variables) to long (one observation for each transition for which a subject is at risk for). We develop a new estimation command, stms, that allows the user to fit different parametric distributions for different transitions, simultaneously, while allowing for sharing of covariate effects across transitions. Finally, predictms calculates transition probabilities, and many other useful measures of absolute risk, following the fit of any model using streg, stms, or stcox, using either a simulation approach or the Aalen–Johansen estimator. We illustrate the software using a dataset of patients with primary breast cancer.