Premium
Penalized regression for interval‐censored times of disease progression: Selection of HLA markers in psoriatic arthritis
Author(s) -
Wu Ying,
Cook Richard J.
Publication year - 2015
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12302
Subject(s) - psoriatic arthritis , lasso (programming language) , medicine , disease , regression , cohort , confidence interval , arthritis , statistics , computer science , mathematics , world wide web
Summary 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.