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 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推断和统计推断的正式框架，能够支持因果问题的明确界定、所需因果假设的可解释且透明的陈述、稳健推断以及最优精度。因此，该路线图尤其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。本文详细介绍了将纵向因果路线图应用于丹麦国家登记处的案例研究，旨在评估二线糖尿病药物对痴呆症风险的比较效果。具体而言，我们评估了在目标人群中（患有2型糖尿病的成人）若初始使用并持续使用GLP-1受体激动剂（一种二线糖尿病药物）与一系列活跃对照方案相比，反事实的五年累积痴呆症风险的差异。通过使用纵向g公式的迭代条件期望表示作为统计估计量，我们处理了时间依赖性混杂。统计估计采用纵向定向最大似然法，并结合机器学习。我们提供了使用登记数据实施路线图的实用指南，并强调了在长期随访中，即使是在登记数据提供的大样本量背景下，稀有暴露和结局如何给灵活且稳健的估计量带来挑战。我们展示了如何通过模拟来支持谨慎的估计量预设定，从而帮助应对这些挑战。我们发现，与部分而非所有其他二线治疗方案相比，GLP-1RA具有保护作用。