Applying the Causal Roadmap to longitudinal national registry data in Denmark: A case study of second-line diabetes medication and dementia

Nance Nerissa (), Mertens Andrew, Gerds Thomas Alexander, Wang Zeyi, Torp-Pedersen Christian, Mark van der Laan, Kvist Kajsa, Lange Theis, Zareini Bochra and Petersen Maya L.
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Nance Nerissa: Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
Mertens Andrew: Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
Gerds Thomas Alexander: Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
Wang Zeyi: Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
Torp-Pedersen Christian: Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
Mark van der Laan: Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
Kvist Kajsa: Innovative Medical Evidence Generation (IMEG), Novo Nordisk, A/S, Søborg, Denmark
Lange Theis: Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
Zareini Bochra: Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
Petersen Maya L.: Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA

Journal of Causal Inference, 2025, vol. 13, issue 1, 18

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 article, 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 5-year cumulative risk of dementia if a target population of adults with type 2 diabetes had initiated and remained on glucagon-like peptide-1 receptor agonists (GLP1-RA) (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.

Keywords: diabetes; Causal Roadmap; dementia; case study; registry data (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:13:y:2025:i:1:p:18:n:1001

DOI: 10.1515/jci-2024-0014

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