  Stepped wedge cluster-randomized trial (CRTs) designs randomize clusters of
individuals to intervention sequences, ensuring that every cluster eventually
transitions from a control period to receive the intervention under study by
the end of the study period. The analysis of stepped wedge CRTs is usually more
complex than parallel-arm CRTs due to potential secular trends that result in
changing intra-cluster and period-cluster correlations over time. A further
challenge in the analysis of closed-cohort stepped wedge CRTs, which follow
groups of individuals enrolled in each period longitudinally, is the occurrence
of dropout. This is particularly problematic in studies of individuals at high
risk for mortality, which causes non-ignorable missing outcomes. If not
appropriately addressed, missing outcomes from death will erode statistical
power, at best, and bias treatment effect estimates, at worst. Joint
longitudinal-survival models can accommodate informative dropout and
missingness patterns in longitudinal studies. Specifically, within this
framework one directly models the dropout process via a time-to-event submodel
together with the longitudinal outcome of interest. The two submodels are then
linked using a variety of possible association structures. This work extends
linear mixed-effects models by jointly modeling the dropout process to
accommodate informative missing outcome data in closed-cohort stepped wedge
CRTs. We focus on constant intervention and general time-on-treatment effect
parametrizations for the longitudinal submodel and study the performance of the
proposed methodology using Monte Carlo simulation under several data-generating
scenarios. We illustrate the joint modeling methodology in practice by
reanalyzing the `Frail Older Adults: Care in Transition' (ACT) trial, a stepped
wedge CRT of a multifaceted geriatric care model versus usual care in the
Netherlands.
