Analysis of time-to-positivity data in tuberculosis treatment studies: Identifying a new limit of quantification.

Abstract

Background: The BACTEC Mycobacteria Growth Indicator Tube (MGIT) machine is the standard globally for detecting viable mycobacteria in patients' sputum. Samples are observed for no longer than 42 days, at which point the sample is declared 'negative' for tuberculosis (TB). This time to detection of bacterial growth, referred to as time-to-positivity (TTP), is increasingly of interest, not solely as a diagnostic tool but also as a continuous biomarker wherein change in TTP can be used for comparing the bactericidal activity of different TB treatments. However, as a continuous measure, there are oddities in the distribution of TTP values observed, particularly at higher values.

Methods: We explored whether there is evidence to suggest setting an upper limit of quantification for modeling purposes (ULOQ_M) lower than the diagnostic limit of detection (LOD) using data from several TB-PACTS randomized clinical trials and PanACEA MAMS-TB.

Results: Across all trials, less than 7.1% of weekly samples returned TTP measurements between 25 and 42 days. Further, the relative absolute prediction error (%) was highest in this range. When modelling with ULOQ_Ms of 25 and 30 days, estimator precision improved for 23 of 25 regimen-level slopes compared to models using the LOD. Discrimination between regimens based on Bayesian posteriors also improved.

Conclusions: Although TTP measurements between 25 days and the diagnostic LOD may be important for diagnostic purposes, TTP values in this range may not contribute meaningfully to its use as a quantitative measure, particularly when assessing treatment response, and may lead to underpowered clinical trials.