Discrete-time multistate regression models in Stata

Multistate life tables (MSLTs), or multistate survival models, have become a widely used analytical framework among epidemiologists, social scientists, and demographers. MSLTs can be cast in continuous time or discrete time. While the choice between the two approaches depends on the concrete research question and available data, discrete-time models have several appealing features: they are easy to apply; the computational cost is typically low; and today's empirical studies are frequently based on regularly spaced longitudinal data, which naturally suggests modeling in discrete time. Despite these appealing features, Stata community-contributed packages have so far been developed only for continuous-time models (Crowther and Lambert 2017; Metzger and Jones 2018) or for traditional demographic life-table calculations that do not allow for covariate adjustment (Muniz 2020). This presentation introduces the recently published Stata package dtms, which seeks to fill the gap in software availability for discrete-time multistate model estimation. The dtms package provides a well-documented and easy-to-apply set of commands that cover a large set of discrete-time MSLT techniques that currently exist in the literature. It also features inference based on newly derived asymptotic covariance matrices as well as inference on group contrasts.