|
Abstract:
|
Cystic Fibrosis (CF) is a multi-systemic disease resulting from mutations in the Cystic Fibrosis Transmembrane Regulator (CFTR) gene and has major clinical manifestations in the sino-pulmonary and gastro-intestinal tracts. Adult CF patient longitudinal lung function data are used to describe and characterize the dynamics of lung function over time. We fit quantile splines and estimate the rate of change of the lung function over time. The estimated derivatives, along with corresponding summary statistics are used for patient clustering. In addition, clinical phenotyping is important for identifying disease prognosis, responses to therapy, genomic/genetic risk assessment and for metabolomic studies. Informative groupings are identified using a proximity matrix generated by unsupervised Random Forests and clustering by Partitioning around Medoids (PAM).
|
