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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., (© 2019 John Wiley & Sons, Ltd.)

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., (© 2018 John Wiley & Sons, Ltd.)

Early bactericidal activity of tuberculosis drugs is conventionally assessed using statistical regression modeling of colony forming unit (CFU) counts over time. Typically, most CFU counts deviate little from the regression curve, but gross outliers due to erroneous sputum sampling are occasionally present and can markedly influence estimates of the rate of change in CFU count, which is the parameter of interest. A recently introduced Bayesian nonlinear mixed effects regression model was adapted to offer a robust approach that accommodates both outliers and potential skewness in the data. At its most general, the proposed regression model fits the skew Student t distribution to residuals and random coefficients. Deviance information criterion statistics and compound Laplace-Metropolis marginal likelihoods were used to discriminate between alternative Bayesian nonlinear mixed effects regression models. We present a relatively easy method to calculate the marginal likelihoods required to determine compound Laplace-Metropolis marginal likelihoods, by adapting methods available in currently available statistical software. The robust methodology proposed in this paper was applied to data from 6 clinical trials. The results provide strong evidence that the distribution of CFU count is often heavy tailed and negatively skewed (suggesting the presence of outliers). Therefore, we recommend that robust regression models, such as those proposed here, should be fitted to CFU count., (Copyright © 2017 John Wiley & Sons, Ltd.)

Rationale: New regimens to shorten tuberculosis treatment and manage patients with drug-resistant tuberculosis who are infected with HIV are urgently needed. Experimental and clinical evidence suggests that the new drugs bedaquiline (B) and pretomanid (Pa), combined with an existing drug, pyrazinamide (Z), and a repurposed drug, clofazimine (C), may assist treatment shortening of drug-susceptible and drug-resistant tuberculosis., Objectives: To evaluate the 14-day bactericidal activity of C and Z in monotherapy and in combinations with Pa and B., Methods: Groups of 15 treatment-naive, sputum smear-positive patients with pulmonary tuberculosis were randomized to receive combinations of B with Z-C, Pa-Z, Pa-Z-C, and Pa-C, or C or Z alone, or standard combination treatment for 14 days. The primary endpoint was the mean daily fall in log10 Mycobacterium tuberculosis CFU per milliliter sputum estimated by joint nonlinear mixed-effects Bayesian regression modeling., Measurements and Main Results: Estimated activities were 0.167 (95% confidence interval [CI], 0.075-0.257) for B-Pa-Z, 0.151 (95% CI, 0.071-0.232) for standard treatment, 0.124 (95% CI, 0.035-0.214) for B-Z-C, 0.115 (95% CI, 0.039-0.189) for B-Pa-Z-C, and 0.076 (95% CI, 0.005-0.145) for B-Pa-C. Z alone had modest activity (0.036; 95% CI, -0.026 to 0.099). C had no activity alone (-0.017; 95% CI, -0.085 to 0.053) or in combinations. Treatments were well tolerated and safe., Conclusions: B-Pa-Z, including two novel agents without resistance in prevalent M. tuberculosis strains, is a potential new tuberculosis treatment regimen. C had no measurable activity in the first 14 days of treatment. Clinical trial registered with www.clinicaltrials.gov (NCT 01691534).