Improving the efficiency of drug resistant tuberculosis treatment trials: a time-to-event alternative marker for...

Establishing the efficacy of new treatments for rifampicin-resistant tuberculosis (RR-TB) is challenging due to the long-term clinical endpoints of two-year relapse-free survival. This study aimed to evaluate the effect of an alternative indicator of treatment response on sample size requirements and the use of a minimization strategy for randomization.

Sample size estimates were compared when based on the commonly used endpoint of the proportion of patients achieving stable culture conversion (SCC) at 12 weeks versus a novel but corresponding indicator of treatment response based on a model of changes in mycobacterial load (MBL) over time. The non-linear mixed effects model, calibrated using data from a RR-TB cohort in the same setting, included a longitudinal MBL decline, a probabilistic component for mycobacteria presence in sputum, and a time-to-event model for culture positivity. Data were simulated for a prespecified treatment effect to compare the power of detecting the treatment effect for various sample sizes when using the commonly used endpoint and alternative indicator of treatment response. Additionally, the impact of random patient allocation versus a minimization strategy for randomization on covariate imbalance was assessed.

To achieve 80% power, 410 individuals were needed using the commonly used endpoint versus 110 participants when using the non-linear mixed effects model, corresponding to a 73% reduction in sample size. A small sample size results in high baseline covariate imbalance with random treatment group allocation, with a median relative imbalance of 0.104 for 110 participants versus 0.053 for 410 participants. This imbalance was reduced to 0.036 for 110 participants when an adaptive minimization procedure was implemented.

Using a model of mycobacterial burden changes over time as an alternative indicator of treatment response, combined with a minimization procedure during the randomization process, significantly reduced the sample size which could, if validated, enhance the efficiency of RR-TB clinical trial design.