Predicting an Ordinal Response Using Features from High-Throughput Genomic Assays

National Library of Medicine Informatics Lecture Series Ordinal scales are commonly used to measure health status and disease related outcomes. An ordinal outcome takes on one of several categorical levels where there is a clear ordering of the categorical levels but no intrinsic numerical relationship between them. As an example, economic status is often recorded as an ordinal outcome taking on three categorical levels of low, medium, and high income. Notable examples in medicine include stage of cancer, grading the severity of an adverse event, and response of target lesions to chemotherapy. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical likelihood-based ordinal modeling methods have contributed to the analysis of data in which the response categories are ordered and the number of predictor variables is smaller than the sample size. With the emergence of genomic technologies being increasingly applied to identify molecular markers associated with complex disease phenotypes and outcomes, many research studies now include high dimensional feature data where the number of predictor variables greatly exceeds the sample size, so that traditional methods cannot be applied. To fill this void we have developed penalized ordinal response models for classifying and predicting an ordinal response. Additionally, we adapted our method to the longitudinal setting to enable modeling disease ...
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