Penalized Regression for Interval-Censored Times of Disease Progression: Selection of HLA Markers in Psoriatic Arthritis
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Date
2015-09
Authors
Wu, Ying
Cook, Richard J.
Advisor
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Abstract
Times of disease progression are interval-censored when progression status is only known at a series of assessment times. This situation arises routinely in clinical trials and cohort studies when events of interest are only detectable upon imaging, based on blood tests, or upon careful clinical examination. We consider the problem of selecting important prognostic biomarkers from a large set of candidates when disease progression status is only known at irregularly spaced and individual-specific assessment times. Penalized regression techniques (e.g., LASSO, adaptive LASSO, and SCAD) are adapted to handle interval-censored time of disease progression. An expectation–maximization algorithm is described which is empirically shown to perform well. Application to the motivating study of the development of arthritis mutilans in patients with psoriatic arthritis is given and several important human leukocyte antigen (HLA) variables are identified for further investigation.
Description
This is the peer reviewed version of the following article: Wu, Y. and Cook, R. J. (2015), Penalized regression for interval-censored times of disease progression: Selection of HLA markers in psoriatic arthritis. Biometrics, 71: 782–791. doi: 10.1111/biom.12302, which has been published in final form at http://dx.doi.org/10.1111/biom.12302 . This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. The definitive version is available at http://dx.doi.org/10.1111/biom.12302 .
Keywords
EM algorithm, Interval-censoring, LASSO, Penalized regression, SCAD, Variable selection