Semiparametric analysis of a generalized linear model with multiple covariates subject to detection limits.
Z estimation theory
accelerated failure time model
limit of detection
multiple exposures
nonparametric survival estimation
pseudolikelihood
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 10 2022
30 10 2022
Historique:
revised:
10
07
2022
received:
23
10
2020
accepted:
11
07
2022
pubmed:
1
8
2022
medline:
18
10
2022
entrez:
31
7
2022
Statut:
ppublish
Résumé
Studies on the health effects of environmental mixtures face the challenge of limit of detection (LOD) in multiple correlated exposure measurements. Conventional approaches to deal with covariates subject to LOD, including complete-case analysis, substitution methods, and parametric modeling of covariate distribution, are feasible but may result in efficiency loss or bias. With a single covariate subject to LOD, a flexible semiparametric accelerated failure time (AFT) model to accommodate censored measurements has been proposed. We generalize this approach by considering a multivariate AFT model for the multiple correlated covariates subject to LOD and a generalized linear model for the outcome. A two-stage procedure based on semiparametric pseudo-likelihood is proposed for estimating the effects of these covariates on health outcome. Consistency and asymptotic normality of the estimators are derived for an arbitrary fixed dimension of covariates. Simulations studies demonstrate good large sample performance of the proposed methods vs conventional methods in realistic scenarios. We illustrate the practical utility of the proposed method with the LIFECODES birth cohort data, where we compare our approach to existing approaches in an analysis of multiple urinary trace metals in association with oxidative stress in pregnant women.
Identifiants
pubmed: 35909228
doi: 10.1002/sim.9536
pmc: PMC9588684
mid: NIHMS1823688
doi:
Types de publication
Journal Article
Research Support, N.I.H., Intramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
4791-4808Subventions
Organisme : NIEHS NIH HHS
ID : P30 ES017885
Pays : United States
Organisme : Intramural NIH HHS
ID : ZIA ES103307
Pays : United States
Organisme : NIEHS NIH HHS
ID : ZIA ES103307
Pays : United States
Informations de copyright
© 2022 John Wiley & Sons, Ltd.
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