The causal roadmap is a formal framework for causal and statistical inference that supports clear specification of the causal question, interpretable and transparent statement of required causal assumptions, robust inference, and optimal precision. The roadmap is thus particularly well-suited to evaluating longitudinal causal effects using large scale registries; however, application of the roadmap to registry data also introduces particular challenges. In this paper we provide a detailed case study of the longitudinal causal roadmap applied to the Danish National Registry to evaluate the comparative effectiveness of second-line diabetes drugs on dementia risk. Specifically, we evaluate the difference in counterfactual five-year cumulative risk of dementia if a target population of adults with type 2 diabetes had initiated and remained on GLP-1 receptor agonists (a second-line diabetes drug) compared to a range of active comparator protocols. Time-dependent confounding is accounted for through use of the iterated conditional expectation representation of the longitudinal g-formula as a statistical estimand. Statistical estimation uses longitudinal targeted maximum likelihood, incorporating machine learning. We provide practical guidance on the implementation of the roadmap using registry data, and highlight how rare exposures and outcomes over long-term follow up can raise challenges for flexible and robust estimators, even in the context of the large sample sizes provided by the registry. We demonstrate how outcome blind simulations can be used to help address these challenges by supporting careful estimator pre-specification. We find a protective effect of GLP-1RAs compared to some but not all other second-line treatments.

翻译：因果路线图是一个用于因果与统计推断的正式框架，支持因果问题的明确阐述、所需因果假设的可解释且透明的陈述、稳健的推断以及最优的精度。因此，该路线图特别适用于利用大规模登记数据评估纵向因果效应；然而，将路线图应用于登记数据也带来了特定挑战。本文详细展示了纵向因果路线图在丹麦国家登记数据中的应用案例，以评估二线糖尿病药物对痴呆症风险的比较效果。具体而言，我们评估了假设目标人群（2型糖尿病成人患者）起始并持续使用GLP-1受体激动剂（一种二线糖尿病药物）与一系列活性对照方案相比，反事实的五年累积痴呆症风险的差异。时间依赖性混杂通过利用纵向g公式的迭代条件期望表示作为统计估计量来加以考虑。统计估计采用纵向目标最大似然估计，并结合机器学习。我们提供了使用登记数据实施因果路线图的实践指南，并强调在长期随访中罕见暴露和结局如何在即使登记数据提供大样本量的背景下对灵活且稳健的估计器构成挑战。我们展示了如何通过结果盲法模拟支持谨慎的估计器预指定，以帮助应对这些挑战。我们发现与部分（而非全部）其他二线治疗相比，GLP-1RA具有保护效应。