Applying the causal roadmap to longitudinal national Danish registry data: a case study of second-line diabetes medication and dementia

TL;DR

This study applies the causal roadmap to Danish registry data to evaluate how second-line diabetes medications influence dementia risk, demonstrating the framework's practical utility and addressing challenges in long-term causal inference.

Contribution

It provides a detailed case study of implementing the causal roadmap with registry data, including practical guidance and solutions for rare exposures and outcomes.

Findings

GLP-1 receptor agonists show a protective effect against dementia

Challenges in estimating long-term effects with rare events are addressed

Simulation methods support robust estimator selection

Abstract

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.