  Two Cox-based multistate modeling approaches are compared for analyzing a
complex multicohort event history process. The first approach incorporates
cohort information as a fixed covariate, thereby providing a direct estimation
of the cohort-specific effects. The second approach includes the cohort as
stratum variable, thus giving an extra flexibility in estimating the transition
probabilities. Additionally, both approaches may include possible interaction
terms between the cohort and a given prognostic predictor. Furthermore, the
Markov property conditional on observed prognostic covariates is assessed using
a global score test. Whenever departures from the Markovian assumption are
revealed for a given transition, the time of entry into the current state is
incorporated as a fixed covariate, yielding a semi-Markov process. The two
proposed methods are applied to a three-wave dataset of COVID-19-hospitalized
adults in the southern Barcelona metropolitan area (Spain), and the
corresponding performance is discussed. While both semi-Markovian approaches
are shown to be useful, the preferred one will depend on the focus of the
inference. To summarize, the cohort-covariate approach enables an insightful
discussion on the the behavior of the cohort effects, whereas the
stratum-cohort approach provides flexibility to estimate transition-specific
underlying risks according with the different cohorts
