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

Burger, D. A., Schall, R., Jacobs, R. & Chen, D. G., Jul-2019, In : Pharmaceutical Statistics. 18, 4, p. 420-432 13 p.

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  • A generalized Bayesian nonlinear mixed‐effects regression mode

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DOI

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.

Original languageEnglish
Pages (from-to)420-432
Number of pages13
JournalPharmaceutical Statistics
Volume18
Issue number4
Early online date7-Apr-2019
Publication statusPublished - Jul-2019

    Keywords

  • bactericidal activity, Bayesian, longitudinal, mixed-effects, zero inflated, BACTERICIDAL ACTIVITY, STERILIZING ACTIVITIES, POISSON REGRESSION, PYRAZINAMIDE, MOXIFLOXACIN, COMBINATIONS, PRETOMANID, INFERENCE, CULTURE, PA-824

ID: 85442909