Robust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trials

dc.contributor.authorBurger, Divan Aristo
dc.contributor.authorSchall, Robert
dc.contributor.authorChen, Ding-Geng (Din)
dc.date.accessioned2018-10-17T12:03:27Z
dc.date.issued2018-09
dc.description.abstractEarly phase 2 tuberculosis (TB) trials are conducted to characterize the early bactericidal activity (EBA) of anti‐TB drugs. The EBA of anti‐TB drugs has conventionally been calculated as the rate of decline in colony forming unit (CFU) count during the first 14 days of treatment. The measurement of CFU count, however, is expensive and prone to contamination. Alternatively to CFU count, time to positivity (TTP), which is a potential biomarker for long‐term efficacy of anti‐TB drugs, can be used to characterize EBA. The current Bayesian nonlinear mixed‐effects (NLME) regression model for TTP data, however, lacks robustness to gross outliers that often are present in the data. The conventional way of handling such outliers involves their identification by visual inspection and subsequent exclusion from the analysis. However, this process can be questioned because of its subjective nature. For this reason, we fitted robust versions of the Bayesian nonlinear mixed‐effects regression model to a wide range of TTP datasets. The performance of the explored models was assessed through model comparison statistics and a simulation study. We conclude that fitting a robust model to TTP data obviates the need for explicit identification and subsequent “deletion” of outliers but ensures that gross outliers exert no undue influence on model fits. We recommend that the current practice of fitting conventional normal theory models be abandoned in favor of fitting robust models to TTP data.en_ZA
dc.description.departmentStatisticsen_ZA
dc.description.embargo2019-09-01
dc.description.librarianhj2018en_ZA
dc.description.urihttp://wileyonlinelibrary.com/journal/psten_ZA
dc.identifier.citationBurger DA, Schall R, Chen D-G. Robust Bayesian nonlinear mixed-effects modeling of time to positivity in tuberculosis trials. Pharmaceutical Statistics. 2018;17:615–628. https://doi.org/10.1002/pst.1877.en_ZA
dc.identifier.issn1539-1604 (print)
dc.identifier.issn1539-1612 (online)
dc.identifier.other10.1002/pst.1877
dc.identifier.urihttp://hdl.handle.net/2263/66926
dc.language.isoenen_ZA
dc.publisherWileyen_ZA
dc.rights© 2018 John Wiley & Sons, Ltd. This is the pre-peer reviewed version of the following article : 'Robust Bayesian nonlinear mixed-effectsmodeling of time to positivity in tuberculosis trials', Pharmaceutical Statistics, vol. 17, no. 5, pp. 615–628, 2018, doi : 10.1002/pst.1877. The definite version is available at : wileyonlinelibrary.com/journal/pst.en_ZA
dc.subjectHeavy taileden_ZA
dc.subjectMixed effectsen_ZA
dc.subjectNonlinearen_ZA
dc.subjectCombinationsen_ZA
dc.subjectEarly phase 2 tuberculosisen_ZA
dc.subjectTuberculosis (TB)en_ZA
dc.subjectEarly bactericidal activity (EBA)en_ZA
dc.subjectRegression modelsen_ZA
dc.subjectRandomized trialsen_ZA
dc.subjectPyrazinamideen_ZA
dc.subjectDistributionsen_ZA
dc.subjectColony forming unit (CFU)en_ZA
dc.subjectTime to positivity (TTP)en_ZA
dc.subjectBayesian nonlinear mixed‐effects (NLME)en_ZA
dc.titleRobust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trialsen_ZA
dc.typePostprint Articleen_ZA

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