Evidence map›Paper›PMID 41631433›Full record

ArticleBiometrics2026

Long-term memory effects of an incremental blood pressure intervention in a mortal cohort.

Maria Josefsson, Nina Karalija, Michael J Daniels

Abstract read
In one paragraph

Article in Biometrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Maria JosefssonDepartment of Statistics, USBE, Umeå University, SE-90187, Umeå, Sweden.ORCID 0000-0002-1812-3581
Nina KaralijaCenter for Functional Brain Imaging, Umeå University, SE-90187, Umeå, Sweden.
Michael J DanielsDepartment of Statistics, University of Florida, Gainesville, FL 32611, United States.ORCID 0000-0002-9856-9486

Funding

Combining longitudinal cohort studies to examine cardiovascular risk factor trajectories across the adult lifespan and their association with diseaseR01HL158963 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DANIELS, MICHAEL J, SIDDIQUE, JUNED · 2021 to 2024
$2.3M
Bayesian machine learning for complex missing data and causal inference with a focus on cardiovascular and obesity studiesR01HL166324 · NHLBI · UNIVERSITY OF FLORIDA · PI Michael J Daniels · 2023 to 2026
$2.1M
NHLBI NIH HHS R01 HL158963NHLBI NIH HHS R01 HL166324NIH HHS HL158963NIH HHS RO1 HL166324Swedish Research Council K2010-61X-21446-01
6 · The paper itself

Abstract

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).

Indexed as

Antihypertensive AgentsBlood PressureHypertensionBayes TheoremCohort StudiesComputer SimulationHumansLongitudinal StudiesModels, StatisticalAntihypertensive AgentsBARTdropoutG-computationLDARTsurvivaltruncation by death

Identifiers

PMID41631433
PMCPMC12865380

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.