A generalized Bayesian nonlinear mixed-effects regression model for zero-inflated longitudinal count data in tuberculosis trials

Divan Aristo Burger*, Robert Schall, Rianne Jacobs, Ding Geng Chen

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

2 Citaten (Scopus)
7 Downloads (Pure)


In this paper, we investigate Bayesian generalized nonlinear mixed-effects (NLME) regression models for zero-inflated longitudinal count data. The methodology is motivated by and applied to colony forming unit (CFU) counts in extended bactericidal activity tuberculosis (TB) trials. Furthermore, for model comparisons, we present a generalized method for calculating the marginal likelihoods required to determine Bayes factors. A simulation study shows that the proposed zero-inflated negative binomial regression model has good accuracy, precision, and credibility interval coverage. In contrast, conventional normal NLME regression models applied to log-transformed count data, which handle zero counts as left censored values, may yield credibility intervals that undercover the true bactericidal activity of anti-TB drugs. We therefore recommend that zero-inflated NLME regression models should be fitted to CFU count on the original scale, as an alternative to conventional normal NLME regression models on the logarithmic scale.

Originele taal-2English
Pagina's (van-tot)420-432
Aantal pagina's13
TijdschriftPharmaceutical Statistics
Nummer van het tijdschrift4
Vroegere onlinedatum7-apr-2019
StatusPublished - jul-2019

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