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Long-term memory effects of an incremental blood pressure intervention in a mortal cohort
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0002-1812-3581
Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).ORCID iD: 0000-0002-8603-9453
Department of Statistics, University of Florida, Gainesville, United States.
2026 (English)In: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 82, no 1, article id ujaf176Article in journal (Refereed) Published
Abstract [en]

In the present study, we examine long-term population-level effects on episodic memory of an intervention over 15 years that reduces systolic blood pressure in individuals with hypertension. A limitation with previous research on the potential risk reduction of such interventions is that they do not properly account for the reduction of mortality rates. Hence, one can only speculate whether the effect is due to changes in memory or changes in mortality. Therefore, we extend previous research by providing both an etiological and a prognostic effect estimate. To do this, we propose a Bayesian semi-parametric estimation approach for an incremental threshold intervention, using the extended G-formula. Additionally, we introduce a novel sparsity-inducing Dirichlet prior for longitudinal data, that exploits the longitudinal structure of the data. We demonstrate the usefulness of our approach in simulations, and compare its performance to other Bayesian decision tree ensemble approaches. In our analysis of the data from the Betula cohort, we found no significant prognostic or etiological effects across all ages. This suggests that systolic blood pressure interventions likely do not strongly affect memory, either at the overall population level or among individuals who would remain alive under both the natural course and the intervention (the always survivor stratum).

Place, publisher, year, edition, pages
Oxford University Press, 2026. Vol. 82, no 1, article id ujaf176
Keywords [en]
BART, dropout, G-computation, LDART, survival, truncation by death
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-249944DOI: 10.1093/biomtc/ujaf176ISI: 001677598000001PubMedID: 41631433Scopus ID: 2-s2.0-105029324543OAI: oai:DiVA.org:umu-249944DiVA, id: diva2:2039726
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare, 2019-01064NIH (National Institutes of Health), RO1 HL166324NIH (National Institutes of Health), HL158963Available from: 2026-02-18 Created: 2026-02-18 Last updated: 2026-02-18Bibliographically approved

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Josefsson, MariaKaralija, Nina

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