ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

Kellie J. Archer, Anna Eames Seffernick, Shuai Sun and Yiran Zhang
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Kellie J. Archer: Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, USA
Anna Eames Seffernick: Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, USA
Shuai Sun: Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, USA
Yiran Zhang: Amgen Inc., 1 Amgen Center Dr, Thousand Oaks, CA 91320, USA

Stats, 2022, vol. 5, issue 2, 1-14

Abstract: The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P > N , such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.

Keywords: cumulative logit; penalized models; LASSO; variable inclusion indicators; spike-and-slab (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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