ArticleStatistics in medicine2013
Bayesian semiparametric mixture Tobit models with left censoring, skewness, and covariate measurement errors.
Article in Statistics in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A nutritionally informed model for Bayesian variable selection with metabolite response variables.Biostatistics (Oxford, England) · 2026Article
- Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data.BMC medical research methodology · 2024Article
- Statistical tests for latent class in censored data due to detection limit.Statistical methods in medical research · 2020Article
- New approaches for censored longitudinal data in joint modelling of longitudinal and survival data, with application to HIV vaccine studies.Lifetime data analysis · 2019Review
- Flexible longitudinal linear mixed models for multiple censored responses data.Statistics in medicine · 2019Article
- A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit.International journal of statistics in medical research · 2014Article
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Authors and funding
2 authors.
Funding
Abstract
Common problems to many longitudinal HIV/AIDS, cancer, vaccine, and environmental exposure studies are the presence of a lower limit of quantification of an outcome with skewness and time-varying covariates with measurement errors. There has been relatively little work published simultaneously dealing with these features of longitudinal data. In particular, left-censored data falling below a limit of detection may sometimes have a proportion larger than expected under a usually assumed log-normal distribution. In such cases, alternative models, which can account for a high proportion of censored data, should be considered. In this article, we present an extension of the Tobit model that incorporates a mixture of true undetectable observations and those values from a skew-normal distribution for an outcome with possible left censoring and skewness, and covariates with substantial measurement error. To quantify the covariate process, we offer a flexible nonparametric mixed-effects model within the Tobit framework. A Bayesian modeling approach is used to assess the simultaneous impact of left censoring, skewness, and measurement error in covariates on inference. The proposed methods are illustrated using real data from an AIDS clinical study. .
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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.