Longitudinal Ordinal Models as a General Framework for Medical Outcomes

Univariate ordinal models can be used to model a wide variety of longitudinal outcomes, using only standard software, through the use of Markov processes. This talk will show how longitudinal ordinal models unify a wide variety of types of analyses including time to event, recurrent events, continuous responses interrupted by events, and multiple events that are capable of being placed in a hierarchy. Through the use of marginalization over the previous state in an ordinal multi-state transition model, one may obtain virtually any estimand of interest. Both frequentist and Bayesian methods can be used to fit the model and draw inferences.

Frank Harrell


December 18, 2021