Penalized regression for interval-censored times of disease progression: Selection of HLA markers in psoriatic arthritis

Biometrics
Ying Wu, Richard J Cook

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.

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Citations

Feb 27, 2017·Statistics in Medicine·Ying Wu, Richard J Cook
Jun 18, 2019·Statistical Methods in Medical Research·Chenxi LiDavid Todem
Feb 23, 2020·Biometrical Journal. Biometrische Zeitschrift·Hui ZhaoJianguo Sun
Jul 18, 2020·Biometrical Journal. Biometrische Zeitschrift·Qiang HuLeslie L Robison
Feb 20, 2017·Lifetime Data Analysis·Ying Wu, Richard J Cook
Mar 15, 2020·Biostatistics·Ying Wu, Richard J Cook
Aug 4, 2020·Journal of the American Statistical Association·Hui ZhaoJianguo Sun

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