Objectives: To evaluate whether methodological optimization of serial sputum colony counting (SSCC) studies, a potentially important component in the drug development process for tuberculosis, could significantly improve their power.
Methods: Simulations were carried out using a model derived from a large SSCC dataset. Variance inflation factors (VIFs) were calculated for model parameters, focusing on the elimination rate constant likely to reflect 'sterilizing' activity and sampling schemes were optimized relative to a scheme of daily sampling during the initial phase of therapy. Corresponding sample sizes required for SSCC studies using different schemes were also computed.
Results: Published sampling schemes lacked efficiency with respect to the 'sterilizing' phase. Pragmatic optimized schemes yielding greatest precision were achieved using eleven sampling points around a skeleton of 0, 2, 7, 14 and 56 days. The standard error of the 'sterilizing' rate constant was reduced more than 4-fold, and sample size for realistic treatment effects was effectively halved. Even schemes with a restricted duration of sampling to avoid high proportions of missing data and those with fewer sampling points still achieved significant gains in precision. Sensitivity analysis suggested that such schemes should continue to perform well over the immediately foreseeable range of improvements in therapy.
Conclusions: Methodological improvements in the design of SSCC studies could make them a powerful tool in Phase II development of anti-tuberculosis agents.