Modeling an Ordinal Response when Confronted with a High-Dimensional Feature Space
Kellie Archer, Division of Biostatistics, College of Public Health, The Ohio State University
Pathological evaluations are frequently reported on an ordinal scale. Moreover, diseases may progress from less to more advanced stages. For example, cervical cancer due to HPV infection progresses from normal epithelium, to low-grade squamous intraepithelial lesions, to high-grade squamous intraepithelial lesions (HSIL), and then to invasive carcinoma. To elucidate molecular mechanisms associated with disease progression, genomic characteristics from samples procured from these different tissue types are often assayed using a high-throughput platform. Such assays result in a high-dimensional feature space where the number of predictors (P) greatly exceeds the available sample size (N). In this talk, various approaches to modeling an ordinal response when P>N will be described.
Note: Seminars are free and open to the public. Reception to follow.