title: A Bayesian Nonlinear Mixed-Effects Regression Model for the Characterization of Early Bactericidal Activity of Tuberculosis Drugs.
abstract: Trials of the early bactericidal activity (EBA) of tuberculosis (TB) treatments assess the decline, during the first few days to weeks of treatment, in colony forming unit (CFU) count of Mycobacterium tuberculosis in the sputum of patients with smear-microscopy-positive pulmonary TB. Profiles over time of CFU data have conventionally been modeled using linear, bilinear, or bi-exponential regression. We propose a new biphasic nonlinear regression model for CFU data that comprises linear and bilinear regression models as special cases and is more flexible than bi-exponential regression models. A Bayesian nonlinear mixed-effects (NLME) regression model is fitted jointly to the data of all patients from a trial, and statistical inference about the mean EBA of TB treatments is based on the Bayesian NLME regression model. The posterior predictive distribution of relevant slope parameters of the Bayesian NLME regression model provides insight into the nature of the EBA of TB treatments; specifically, the posterior predictive distribution allows one to judge whether treatments are associated with monolinear or bilinear decline of log(CFU) count, and whether CFU count initially decreases fast, followed by a slower rate of decrease, or vice versa.
authors.affiliation: a Department of Mathematical Statistics and Actuarial Science , University of the Free State , Bloemfontein , South Africa.|b Quintiles Biostatistics , Bloemfontein , South Africa.
authors.forename: Divan Aristo
authors.initials: DA
authors.lastname: Burger
authors.affiliation: a Department of Mathematical Statistics and Actuarial Science , University of the Free State , Bloemfontein , South Africa.|b Quintiles Biostatistics , Bloemfontein , South Africa.
authors.forename: Robert
authors.initials: R
authors.lastname: Schall
mesh_terms: D000465:Algorithms; D000995:Antitubercular Agents / Q000494:pharmacology*; D058491:Bacterial Load; D001499:Bayes Theorem; D015169:Colony Count, Microbial; D008826:Microbial Sensitivity Tests / Q000706:statistics & numerical data; D015233:Models, Statistical; D009169:Mycobacterium tuberculosis / Q000187:drug effects*; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D012044:Regression Analysis; D014397:Tuberculosis, Pulmonary / Q000382:microbiology
chemical_list: D000995:Antitubercular Agents
keywords: Bayesian nonlinear mixed-effects (NLME) regression model; Biphasic; Colony forming unit (CFU) count; Early bactericidal activity (EBA); Tuberculosis (TB).