<p>Clustering details from recent TB studies.</p>

However, such pairwise analyses are computationally intensive, and may not be feasible to conduct given the high dyad count in even moderately sized datasets. Here we compare two approaches to increase the efficiency of pairwise analysis for large datasets.

We quantify and compare the performance of divide-and-conquer Bayesian model fitting and pairwise case-control approaches for estimating associations between individual- and pair-level covariates and shared membership in a transmission cluster. We utilize a large dataset (n = 4,154) of spatially-referenced, genomically-sequenced <i>Mycobacterium tuberculosis</i> isolates collected from a single city for this analysis, and conduct simulation studies under three representative tuberculosis genomic clustering settings.