ArticleStatistics in medicine2022
A semi-parametric Bayesian model for semi-continuous longitudinal data.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- The longitudinal structure of cognition in the ABCD study and associations with neural function and psychopathology: A Bayesian probabilistic principal components analysis.Developmental cognitive neuroscience · 2026Article
- Normative modeling of MEG brain oscillations across the human lifespan.Communications biology · 2026Article
- A systematic review of operational research modelling for alcohol consumption and its consequences.Health systems (Basingstoke, England) · 2026Review
- Initial Subjective Response to Nicotine Vaping Predicts Subsequent E-Cigarette Use in Early Adolescence: An ABCD Investigation.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025Article
- Deep Mixture of Linear Mixed Models for Complex Longitudinal Data.Statistics in medicine · 2025Article
- Longitudinal analysis of the ABCD® study.Developmental cognitive neuroscience · 2025Review
- Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes.Communications biology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
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.