Abstract
The influence of rare variants in the concurrent drug usage on the outcome of interest has not been analyzed in-depth. The standard method of analysis has only encompassed testing of the significance of common variants in concurrent drug usage, comorbidities, and genetic markers. This study proposes the application of state of the art association analysis tools: Combined Multivariate Collapsing (CMC), Kernel-Based Adaptive Clustering (K-BAC), and Weighted Sum Statistics (WSS) algorithms, for testing association of patient drug usage description data to drug treatment outcome. We demonstrate the usefulness of this novel approach in detecting significant rare descriptive data sequences with an analysis of a pharmacology dataset, featuring patients treated with Warfarin. This manuscript presents an examination of the association of comorbidities and concurrent drug usage with reaching stable dosage levels of Warfarin. Statistically significant results will reveal important, undocumented associations between concurrent drug usage and a patient’s ability to reach stable dosage levels of Warfarin. We consider rare variants in self-governed medical decisions, which lead to significant difference in dosage stability outcomes. Significant results are found both in rare variants of concurrent drug usage which provide higher probability of either reaching or not reaching stable dosage levels of Warfarin.