Pair Matcher (PaM): Fast Model-Based Optimisation Of Treatment/Case-Control Matches Using Demographic And Genetic Data

In clinical trials, individuals are matched for demographic criteria, paired, and then randomly assigned to treatment and control groups to determine a drugs efficacy. The successful completion of pilot trials is a prerequisite to larger and more expensive Phase III trials. One of the chief causes 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. Pair Matcher (PaM) addresses stratification bias by optimising pairing assignments a priori- and\\or 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 racial criteria or Principal Component Analysis (PCA) alone. Homogenising the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the study. PaMs ability to infer the ancestry of the participants further allows identifying subgroup of responders and developing a precision medicine approach to treatment. PaM is simple to execute, fast, and can be used for clinical trials and association studies. PaM is freely available via R scripts and a web interface.