A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials.

TitleA new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials.
Publication TypeJournal Article
Year of Publication2019
AuthorsWu, Jing, Ming-Hui Chen, Elizabeth D. Schifano, Joseph G. Ibrahim, and Jeffrey D. Fisher
JournalStat Med
Volume38
Issue30
Pagination5565-5586
Date Published2019 12 30
ISSN1097-0258
KeywordsBayes Theorem, Biostatistics, Computer Simulation, Data Interpretation, Statistical, Female, HIV Infections, Humans, Likelihood Functions, Longitudinal Studies, Male, Models, Statistical, Patient Dropouts, Poisson Distribution, Randomized Controlled Trials as Topic, Regression Analysis, Sexual Behavior
Abstract

In longitudinal clinical trials, it is common that subjects may permanently withdraw from the study (dropout), or return to the study after missing one or more visits (intermittent missingness). It is also routinely encountered in HIV prevention clinical trials that there is a large proportion of zeros in count response data. In this paper, a sequential multinomial model is adopted for dropout and subsequently a conditional model is constructed for intermittent missingness. The new model captures the complex structure of missingness and incorporates dropout and intermittent missingness simultaneously. The model also allows us to easily compute the predictive probabilities of different missing data patterns. A zero-inflated Poisson mixed-effects regression model is assumed for the longitudinal count response data. We also propose an approach to assess the overall treatment effects under the zero-inflated Poisson model. We further show that the joint posterior distribution is improper if uniform priors are specified for the regression coefficients under the proposed model. Variations of the g-prior, Jeffreys prior, and maximally dispersed normal prior are thus established as remedies for the improper posterior distribution. An efficient Gibbs sampling algorithm is developed using a hierarchical centering technique. A modified logarithm of the pseudomarginal likelihood and a concordance based area under the curve criterion are used to compare the models under different missing data mechanisms. We then conduct an extensive simulation study to investigate the empirical performance of the proposed methods and further illustrate the methods using real data from an HIV prevention clinical trial.

DOI10.1002/sim.8379
Alternate JournalStat Med
Original PublicationA new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials.
PubMed ID31691322
PubMed Central IDPMC6891130
Grant ListR01 GM070335 / GM / NIGMS NIH HHS / United States
R01 MH077524 / MH / NIMH NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: