A multivariate joint model to adjust for random measurement error while handling skewness and correlation in dietary data in an epidemiologic study of mortality.


Journal

Annals of epidemiology
ISSN: 1873-2585
Titre abrégé: Ann Epidemiol
Pays: United States
ID NLM: 9100013

Informations de publication

Date de publication:
06 2023
Historique:
received: 03 12 2022
revised: 20 03 2023
accepted: 23 03 2023
pmc-release: 01 06 2024
medline: 5 6 2023
pubmed: 28 3 2023
entrez: 27 3 2023
Statut: ppublish

Résumé

A substantial proportion of global deaths is attributed to unhealthy diets, which can be assessed at baseline or longitudinally. We demonstrated how to simultaneously correct for random measurement error, correlations, and skewness in the estimation of associations between dietary intake and all-cause mortality. We applied a multivariate joint model (MJM) that simultaneously corrected for random measurement error, skewness, and correlation among longitudinally measured intake levels of cholesterol, total fat, dietary fiber, and energy with all-cause mortality using US National Health and Nutrition Examination Survey linked to the National Death Index mortality data. We compared MJM with the mean method that assessed intake levels as the mean of a person's intake. The estimates from MJM were larger than those from the mean method. For instance, the logarithm of hazard ratio for dietary fiber intake increased by 14 times (from -0.04 to -0.60) with the MJM method. This translated into a relative hazard of death of 0.55 (95% credible interval: 0.45, 0.65) with the MJM and 0.96 (95% credible interval: 0.95, 0.97) with the mean method. MJM adjusts for random measurement error and flexibly addresses correlations and skewness among longitudinal measures of dietary intake when estimating their associations with death.

Identifiants

pubmed: 36972757
pii: S1047-2797(23)00056-X
doi: 10.1016/j.annepidem.2023.03.007
pmc: PMC10239394
mid: NIHMS1886860
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

8-15

Subventions

Organisme : NIA NIH HHS
ID : P30 AG021342
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002014
Pays : United States

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest All the authors have no conflict of interest to declare for this research.

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Auteurs

George O Agogo (GO)

StatsDecide Analytics and Consulting Ltd, Nakuru, Kenya. Electronic address: agogogeorge@gmail.com.

Leacky Muchene (L)

StatsDecide Analytics and Consulting Ltd, Nakuru, Kenya.

Benedict Orindi (B)

Department of Statistics, Center for Geographic Medicine Research, KEMRI-Wellcome Trust, Kilifi, Kenya.

Terrence E Murphy (TE)

Public Health Sciences, Pennsylvania State University College of Medicine, Hershey.

Henry Mwambi (H)

School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg Campus, Pietermaritzburg, South Africa.

Heather G Allore (HG)

Department of Internal Medicine, Section of Geriatrics, Yale School of Medicine, New Haven, CT; Department of Biostatistics, Yale School of Public Health, New Haven, CT.

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Classifications MeSH