Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data.

ABSTRACT: Abstract An ordinal scale is commonly used to measure health status and disease related outcomes in hospital settings as well as in translational medical research. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical methodology based on statistical inference, in particular, ordinal modeling has contributed to the analysis of data in which the response categories are ordered and the number of covariates (p) remains smaller than the sample size (n). With the emergence of genomic technologies being increasingly applied for more accurate diagnosis and prognosis, high-dimensional data where the number of covariates (p) is much larger than the number of samples (n), are generated. To meet the emerging needs

SUBMITTER: Hou J 

PROVIDER: S-EPMC4454613 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature