A generalized Bayesian nonlinear mixed-effects regression model for zero-inflated longitudinal count data in tuberculosis trials
| Autor: | Divan Aristo Burger, Rianne Jacobs, Ding-Geng Chen, Robert Schall |
|---|---|
| Přispěvatelé: | Stochastic Studies and Statistics |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
longitudinal BACTERICIDAL ACTIVITY Antitubercular Agents Colony Count Microbial Negative binomial distribution POISSON REGRESSION Poisson distribution 01 natural sciences Bayesian CULTURE 010104 statistics & probability 03 medical and health sciences symbols.namesake 0302 clinical medicine mixed-effects Statistics Credible interval STERILIZING ACTIVITIES Humans Tuberculosis Pharmacology (medical) 030212 general & internal medicine Poisson regression MOXIFLOXACIN 0101 mathematics Mathematics Pharmacology PYRAZINAMIDE PRETOMANID Models Statistical PA-824 COMBINATIONS Contrast (statistics) Bayes Theorem Regression analysis Bayes factor Nonlinear Dynamics zero inflated symbols INFERENCE Count data |
| Zdroj: | Pharmaceutical Statistics, 18(4), 420-432 |
| ISSN: | 1539-1604 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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