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.

翻译：阶梯楔形整群随机试验设计将个体群组随机分配至干预序列，确保每个群组在研究周期结束时最终从对照期过渡到接受研究干预。由于潜在的时间趋势导致群内相关性和时期-群组相关性随时间变化，阶梯楔形CRT的分析通常比平行臂CRT更为复杂。在闭群阶梯楔形CRT中（纵向追踪每个时期纳入的个体群组），脱落问题的出现构成了进一步挑战。对于高死亡风险人群的研究尤为棘手，非可忽略的结局缺失会降低统计效力甚至导致处理效应估计偏倚。纵向-生存联合模型可处理纵向研究中存在信息性脱落和缺失模式的问题。具体而言，该框架通过构建时间-事件子模型直接对脱落过程建模，同时联合分析感兴趣的纵向结局，并通过多种关联结构连接两个子模型。本研究通过联合建模脱落过程扩展线性混合效应模型，以处理闭群阶梯楔形CRT中的信息性结局缺失。我们重点研究了纵向子模型的恒定干预效应和一般治疗时间效应参数化方法，并通过蒙特卡洛模拟在多种数据生成场景下评估了所提方法的性能。通过重新分析荷兰"体弱老年人：护理转型"试验（一项评估综合老年护理模式与常规护理的阶梯楔形CRT），我们展示了联合建模方法在实际中的应用。