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

• Published in: Statistics in medicine Vol. 38; no. 30; pp. 5565 - 5586
• Main Authors: Wu, Jing, Chen, Ming‐Hui, Schifano, Elizabeth D., Ibrahim, Joseph G., Fisher, Jeffrey D.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 30.12.2019
• Subjects: AUC; Bayes Theorem; Bayesian analysis; Biostatistics; Clinical trials; Computer Simulation; Data Interpretation, Statistical; Disease prevention; Female; G‐prior; HIV; HIV Infections - prevention & control; HIV Infections - psychology; HIV Infections - transmission; Human immunodeficiency virus; Humans; Jeffreys prior; Likelihood Functions; Longitudinal Studies; LPML; Male; maximally dispersed normal prior; Medical statistics; Models, Statistical; Patient Dropouts - statistics & numerical data; Poisson Distribution; Randomized Controlled Trials as Topic - statistics & numerical data; Regression Analysis; Sexual Behavior; zero‐inflated Poisson; G-prior; LPML; maximally dispersed normal prior; zero-inflated Poisson; Jeffreys prior; AUC
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.8379

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.