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

Skip Navigation

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

Loading... Loading video...

182 Views

Air date:	Wednesday, June 15, 2016, 2:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 182 (54 Live, 128 On-demand)
Category:	Special
Runtime:	01:02:58
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	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 progression along with time. We demonstrate our methods using data from two different studies that used high-throughput genomic platforms, the Illumina GoldenGate Methylation BeadArray and Affymetrix gene expression profiles. Dr. Kellie Archer is a Professor in the Department of Biostatistics and Director of the Massey Cancer Center Biostatistics Shared Resource at Virginia Commonwealth University. She completed her PhD at The Ohio State University and previously worked there in support of research associated with the Cancer and Leukemia Group B (CALGB) Leukemia Correlative Sciences Committee. She now works primarily in developing innovative statistical methods and software for the analysis of high-dimensional datasets such as those arising from high-throughput genomic platforms. Dr. Archer is the author or co-author of 107 published papers, two book chapters, over 30 university seminars/professional conference presentations. She holds an editorial appointment at Progress in Transplantation and is a Statistical Consultant for Radiology and the Nature Publishing Group. She serves as a member of the National Library of Medicine Biomedical Library and Informatics Review Committee.

NLM Title:	Predicting an ordinal response using features from high-throughput genomic assays / Kellie J. Archer.
Author:	Archer, Kellie J. National Institutes of Health (U.S.),
Publisher:
Abstract:	(CIT): 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 progression along with time. We demonstrate our methods using data from two different studies that used high-throughput genomic platforms, the Illumina GoldenGate Methylation BeadArray and Affymetrix gene expression profiles. Dr. Kellie Archer is a Professor in the Department of Biostatistics and Director of the Massey Cancer Center Biostatistics Shared Resource at Virginia Commonwealth University. She completed her PhD at The Ohio State University and previously worked there in support of research associated with the Cancer and Leukemia Group B (CALGB) Leukemia Correlative Sciences Committee. She now works primarily in developing innovative statistical methods and software for the analysis of high-dimensional datasets such as those arising from high-throughput genomic platforms. Dr. Archer is the author or co-author of 107 published papers, two book chapters, over 30 university seminars/professional conference presentations. She holds an editorial appointment at Progress in Transplantation and is a Statistical Consultant for Radiology and the Nature Publishing Group. She serves as a member of the National Library of Medicine Biomedical Library and Informatics Review Committee.
Subjects:	Models, Genetic Models, Statistical
Publication Types:	Lecture Webcast

NLM Classification:	WA 950
NLM ID:	101686963
CIT Live ID:	19100
Permanent link:	https://videocast.nih.gov/watch=19100

Related Videos...