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 more complex intra-cluster correlation structures. 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 the joint longitudinal-survival modeling 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 methodology in practice by reanalyzing data from the `Frail Older Adults: Care in Transition' (ACT) trial, a stepped wedge CRT of a multifaceted geriatric care model versus usual care in 35 primary care practices in the Netherlands.

翻译：阶梯式楔形群组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期结束前均能从对照阶段过渡至接受所研究的干预措施。由于群组内相关性结构更为复杂，阶梯式楔形群组随机试验的分析通常比平行臂群组随机试验更为复杂。在封闭队列阶梯式楔形群组随机试验（即对各入组期个体进行纵向追踪的研究）的分析中，脱落现象的出现构成了进一步挑战。这在研究高死亡风险个体时尤为突出，会导致非可忽略的结局缺失。若处理不当，死亡导致的结局缺失至少会削弱统计功效，最坏情况下将导致干预效应估计产生偏倚。联合纵向-生存模型能够处理纵向研究中的信息性脱落与缺失模式。具体而言，在该建模框架下，研究者通过时间-事件子模型与目标纵向结局子模型直接对脱落过程进行建模，随后通过多种可能的关联结构将两个子模型连接起来。本研究通过联合建模脱落过程，扩展了线性混合效应模型，以处理封闭队列阶梯式楔形群组随机试验中的信息性结局缺失数据。我们针对纵向子模型聚焦于恒定干预效应与广义治疗时长效应参数化设定，并通过蒙特卡洛模拟在多种数据生成情境下检验所提方法的性能。通过重新分析“衰弱老年人：过渡期护理”试验（一项在荷兰35家初级诊疗机构中开展的、对比多维度老年护理模式与常规护理的阶梯式楔形群组随机试验）数据，我们在实践中展示了该方法的应用。