June 2024 Functional partial least squares with censored outcomes: Prediction of breast cancer risk with mammogram images
Shu Jiang, Jiguo Cao, Graham A. Colditz
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Ann. Appl. Stat. 18(2): 1051-1063 (June 2024). DOI: 10.1214/23-AOAS1822

Abstract

We consider the problem of predicting breast cancer risk using mammogram imaging data where the dimension of pixels greatly exceed the number of individuals in the cohort. The functional partial least squares (FPLS) is a popular dimensional reduction method in constructing latent explanatory components using linear combinations of the original predictor variables. While FPLS with scalar responses has been studied in the literature, the presence of right censoring under the survival framework poses challenges in modeling and estimation. Given several different representations for PLS with Cox regression in the literature, we unify and extend three formulations to deal with right censoring, that is, reweighing, mean imputation, and deviance residuals to the functional setting in this paper. We empirically investigate and compare the performance of the three proposed FPLS frameworks in the context of imaging predictor via intensive simulation studies. The proposed methods are applied to the Joanne Knight Breast Health Cohort where we show increased model discriminatory performance under the FPLS framework compared to competing models.

Funding Statement

This research is partially supported by the NIH (R37 CA256810), Breast Cancer Research Foundation (BCRF 20-028), and the NSERC Discovery grant (RGPIN-2018-06008).

Acknowledgments

The authors wish to thank the Editor and anonymous referees of this article for their valuable and thoughtful input. These suggestions have significantly enhanced the quality of the manuscript.

Citation

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Shu Jiang. Jiguo Cao. Graham A. Colditz. "Functional partial least squares with censored outcomes: Prediction of breast cancer risk with mammogram images." Ann. Appl. Stat. 18 (2) 1051 - 1063, June 2024. https://doi.org/10.1214/23-AOAS1822

Information

Received: 1 February 2023; Revised: 1 August 2023; Published: June 2024
First available in Project Euclid: 5 April 2024

Digital Object Identifier: 10.1214/23-AOAS1822

Keywords: Functional partial least squares , image analysis , risk prediction , Survival analysis

Rights: Copyright © 2024 Institute of Mathematical Statistics

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Vol.18 • No. 2 • June 2024
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