Pair Matcher (PaM): fast model-based optimisation of treatment/case-control matches

Motivation: In clinical trials, individuals are matched using demographic criteria, paired, and then randomly assigned to treatment and control groups to determine a drug’s efficacy. A chief cause for the irreproducibility of results across pilot to Phase III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups. Results: Pair Matcher (PaM) addresses stratification bias by optimising pairing assignments a priori and/or a posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on competing methods, including the commonly used principal component analysis (PCA). Homogenising the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the trial or genetic study. PaM’s ancestral inferences also allow characterizing responders and developing a precision medicine approach to treatment.