Robust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trials. (19th July 2018)

• Record Type: Journal Article
• Authors: Burger, Divan Aristo; Schall, Robert; Chen, Ding‐Geng
• Is Part Of: Pharmaceutical statistics. Volume 17:Number 5(2018)
• Page Start: 615
• Page End: 628
• Publication Date: 2018-07-19
• Subjects: Bayesian -- heavy tailed -- mixed effects -- nonlinear -- time to positivity; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1877
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract : Early 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.