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Activity Number: 499
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #313001
Title: Strategy for Modeling Nonrandom Missing Data Mechanisms in High-Throughput Omics Studies
Author(s): Kyoungmi Kim*+ and Sandra L. Taylor
Companies: University of California, Davis and University of California, Davis
Keywords: point-mass mixture ; accelerated failure time model ; nonrandom missing values ; high-throughput omics data ; mass spectrometry ; glycomics
Abstract:

Mass spectrometry (MS) is a high-throughput analytic technique for profiling small molecular compounds in biological samples and is widely used to identify potential diagnostic and prognostic compounds or to elucidate the chemical structures of molecules associated with disease. Commonly, this data generated by mass spectrometry has many missing values resulting when a compound is absent from a sample or is present but at a concentration below the detection limit which is missing not at random. Several strategies are available for statistically analyzing MS-generated omics data with missing values. The accelerated failure time (AFT) model assumes all missing values result from censoring below a detection limit. Under a mixture model, missing values can result from a combination of censoring and the absence of a compound. We compare power and estimation of a mixture model to an AFT model using simulations. Based on findings, we suggest a hybrid approach of using the AFT model for hypothesis testing and mixture model for estimation. We demonstrate this approach through application to real glycomics data of serum samples obtained from a case-control study.


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