A generalized Bayesian nonlinear mixed-effects regression model for zero-inflated longitudinal count data in tuberculosis trialsBurger, D. A., Schall, R., Jacobs, R. & Chen, D. G., Jul-2019, In : Pharmaceutical Statistics. 18, 4, p. 420-432 13 p.
Research output: Contribution to journal › Article › Academic › peer-review
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.
|Number of pages||13|
|Early online date||7-Apr-2019|
|Publication status||Published - Jul-2019|
- bactericidal activity, Bayesian, longitudinal, mixed-effects, zero inflated, BACTERICIDAL ACTIVITY, STERILIZING ACTIVITIES, POISSON REGRESSION, PYRAZINAMIDE, MOXIFLOXACIN, COMBINATIONS, PRETOMANID, INFERENCE, CULTURE, PA-824