A semi-parametric Bayesian model for semi-continuous longitudinal data.
B-spline
Bayesian
Markov chain Monte Carlo
longitudinal
semi-continuous
semi-parametric
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
15 06 2022
15 06 2022
Historique:
revised:
21
01
2022
received:
15
06
2021
accepted:
03
02
2022
pubmed:
12
3
2022
medline:
22
4
2022
entrez:
11
3
2022
Statut:
ppublish
Résumé
Semi-continuous data present challenges in both model fitting and interpretation. Parametric distributions may be inappropriate for extreme long right tails of the data. Mean effects of covariates, susceptible to extreme values, may fail to capture relevant information for most of the sample. We propose a two-component semi-parametric Bayesian mixture model, with the discrete component captured by a probability mass (typically at zero) and the continuous component of the density modeled by a mixture of B-spline densities that can be flexibly fit to any data distribution. The model includes random effects of subjects to allow for application to longitudinal data. We specify prior distributions on parameters and perform model inference using a Markov chain Monte Carlo (MCMC) Gibbs-sampling algorithm programmed in R. Statistical inference can be made for multiple quantiles of the covariate effects simultaneously providing a comprehensive view. Various MCMC sampling techniques are used to facilitate convergence. We demonstrate the performance and the interpretability of the model via simulations and analyses on the National Consortium on Alcohol and Neurodevelopment in Adolescence study (NCANDA) data on alcohol binge drinking.
Identifiants
pubmed: 35274335
doi: 10.1002/sim.9359
pmc: PMC9035098
mid: NIHMS1780096
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
2354-2374Subventions
Organisme : NIH HHS
ID : MH120025
Pays : United States
Organisme : NIAAA NIH HHS
ID : U24 AA021697
Pays : United States
Organisme : NIAAA NIH HHS
ID : U01 AA021692
Pays : United States
Organisme : NIH HHS
ID : 5U24AA021695-09
Pays : United States
Organisme : NIAAA NIH HHS
ID : U24 AA021695
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH122688
Pays : United States
Organisme : NIH HHS
ID : 5U24AA021697-09
Pays : United States
Organisme : NIMH NIH HHS
ID : RF1 MH120025
Pays : United States
Organisme : NIH HHS
ID : MH122688
Pays : United States
Informations de copyright
© 2022 John Wiley & Sons Ltd.
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