Applying the Causal Roadmap to longitudinal national registry data in Denmark: A case study of second-line diabetes medication and dementia
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| Název: | Applying the Causal Roadmap to longitudinal national registry data in Denmark: A case study of second-line diabetes medication and dementia |
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| Autoři: | Nance Nerissa, Mertens Andrew, Gerds Thomas Alexander, Wang Zeyi, Torp-Pedersen Christian, van der Laan Mark, Kvist Kajsa, Lange Theis, Zareini Bochra, Petersen Maya L. |
| Zdroj: | Journal of Causal Inference, Vol 13, Iss 1, Pp 616-9 (2025) |
| Informace o vydavateli: | De Gruyter, 2025. |
| Rok vydání: | 2025 |
| Sbírka: | LCC:Mathematics LCC:Probabilities. Mathematical statistics |
| Témata: | diabetes, causal roadmap, dementia, case study, registry data, 62-07, Mathematics, QA1-939, Probabilities. Mathematical statistics, QA273-280 |
| Popis: | 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. |
| Druh dokumentu: | article |
| Popis souboru: | electronic resource |
| Jazyk: | English |
| ISSN: | 2193-3685 |
| Relation: | https://doaj.org/toc/2193-3685 |
| DOI: | 10.1515/jci-2024-0014 |
| Přístupová URL adresa: | https://doaj.org/article/87b170cd4b334072a5589d88b1f03715 |
| Přístupové číslo: | edsdoj.87b170cd4b334072a5589d88b1f03715 |
| Databáze: | Directory of Open Access Journals |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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. |
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| ISSN: | 21933685 |
| DOI: | 10.1515/jci-2024-0014 |