Bayesian Tobit Modeling of Longitudinal Ordinal Clinical Trial Compliance Data with Nonignorable Missingness
作者:
MaryKathryn Cowles,
BradleyP. Carlin,
JohnE. Connett,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 433
页码: 86-98
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476666
出版商: Taylor & Francis Group
关键词: Gibbs sampling;Repeated measures
数据来源: Taylor
摘要:
In the Lung Health Study (LHS), compliance with the use of inhaled medication was assessed at each follow-up visit both by self-report and by weighing the used medication canisters. One or both of these assessments were missing if the participant failed to attend the visit or to return all canisters. Approximately 30% of canister-weight data and 5% to 15% of self-report data were missing at different visits. We use Gibbs sampling with data augmentation and a multivariate Hastings update step to implement a Bayesian hierarchical model for LHS inhaler compliance. Incorporating individual-level random effects to account for correlations among repeated measures on the same participant, our model is a longitudinal extension of the Tobit models used in econometrics to deal with partially unobservable data. It enables (a) assessment of the relationships among visit attendance, canister return, self-reported compliance level, and canister weight compliance, and (b) determination of demographic, physiological, and behavioral predictors of compliance. In addition to addressing the estimation and prediction questions of substantive interest, we use sampling-based methods for covariate screening and model selection and investigate a range of informative priors on missing data.
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