Missing data in multiple variables is a common issue. We investigate the applicability of the framework of graphical models for handling missing data to a complex longitudinal pharmacological study of children with HIV treated with an efavirenz-based regimen as part of the CHAPAS-3 trial. Specifically, we examine whether the causal effects of interest, defined through static interventions on multiple continuous variables, can be recovered (estimated consistently) from the available data only. So far, no general algorithms are available to decide on recoverability, and decisions have to be made on a case-by-case basis. We emphasize sensitivity of recoverability to even the smallest changes in the graph structure, and present recoverability results for three plausible missingness directed acyclic graphs (m-DAGs) in the CHAPAS-3 study, informed by clinical knowledge. Furthermore, we propose the concept of ``closed missingness mechanisms'' and show that under these mechanisms an available case analysis is admissible for consistent estimation for any type of statistical and causal query, even if the underlying missingness mechanism is of missing not at random (MNAR) type. Both simulations and theoretical considerations demonstrate how, in the assumed MNAR setting of our study, a complete or available case analysis can be superior to multiple imputation, and estimation results vary depending on the assumed missingness DAG. Our analyses demonstrate an innovative application of missingness DAGs to complex longitudinal real-world data, while highlighting the sensitivity of the results with respect to the assumed causal model.

翻译：多重变量缺失是常见问题。本研究探讨了图形模型处理缺失数据的框架在复杂纵向药理学研究中的适用性，该研究以CHAPAS-3试验中接受依非韦伦方案治疗的HIV感染儿童为对象。具体而言，我们考察了通过多个连续变量的静态干预所定义的因果效应，是否能够仅从现有数据中恢复（即被一致估计）。目前尚无通用算法可判定可恢复性，必须基于具体案例进行分析。我们强调可恢复性对图结构最微小变化的敏感性，并基于临床知识提出了CHAPAS-3研究中三种合理的缺失有向无环图（m-DAG），给出了相应的可恢复性结果。此外，我们提出了“封闭缺失机制”的概念，并证明在此类机制下，即使基础缺失机制属于非随机缺失（MNAR）类型，可用案例分析对于任何统计与因果查询的一致估计都是可接受的。模拟实验与理论分析均表明，在本研究假设的MNAR情境下，完整案例分析或可用案例分析可能优于多重插补法，且估计结果会随假设的缺失DAG不同而变化。我们的分析展示了缺失DAG在复杂纵向真实世界数据中的创新应用，同时凸显了结果对假设因果模型的敏感性。