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Full text release has been delayed at the author's request until September 01, 2025

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Functional Principal Component Analysis for Heterogeneous Survival Data

Talafha, Ahmad
http://orcid.org/0000-0002-8477-995X
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1713798310335436

Abstract Details

2024, Doctor of Philosophy (Ph.D.), Bowling Green State University, Statistics.
Longitudinal biomarkers offer insights into disease progression. Accordingly, during their follow-up appointments, patients' biomarker data are recorded to track these changes over time. To accurately anticipate disease progression, it's imperative to employ statistical models tailored to these longitudinal biomarker datasets, accounting for patient heterogeneity. Traditional models like the Cox proportional hazards model (Cox PH) may not fully capture the intricacies of heterogeneous, time-dependent data due to their inherent assumption of homogeneity. In response, we integrate Functional Principal Component Analysis (FPCA) and Supervised FPCA with the Cox PH mixture model to better handle these challenges. This integration aims to utilize FPCA for extracting meaningful features from longitudinal biomarker data, while Supervised FPCA is employed to improve the relevance of these features to patient outcomes and use these features as covariates in Cox PH mixture model to conduct dynamic predictions. To enhance model adaptability to heterogeneous patient subgroups, we extend the Cox PH framework by incorporating dynamic penalty functions, specifically the Smoothly Clipped Absolute Deviation (SCAD) and the Minimax Concave Penalty (MCP), into a mixture model setting. This approach helps to mitigate the assumption of homogeneity among patient groups. Additionally, we study a modified Expectation Maximization (EM) algorithm, tailored for our Cox PH mixture model, which facilitates the concurrent estimation of model parameters and determination of the appropriate number of mixture components. Our approach provides a structured method for analyzing longitudinal biomarker and survival data, enabling more nuanced predictions that can adapt as new biomarker information becomes available. Through simulation studies and real-world data application, we demonstrate the utility of our method, though noting its predictive performance compared to traditional methods warrants careful evaluation.
John Chen (Committee Chair)
Kei Nomaguchi (Other)
Riddhi Ghosh (Committee Member)
Umar Islambekov (Committee Member)
125 p.
Biostatistics; Statistics
Functional principal component analysis; Supervised Functional principal component analysis; Cox proportional hazards mixture model; smoothly clipped absolute deviation penalty; the minimax concave penalty;

Recommended Citations

Citations

  • Talafha, A. (2024). Functional Principal Component Analysis for Heterogeneous Survival Data [Doctoral dissertation, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1713798310335436

    APA Style (7th edition)

  • Talafha, Ahmad. Functional Principal Component Analysis for Heterogeneous Survival Data. 2024. Bowling Green State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1713798310335436.

    MLA Style (8th edition)

  • Talafha, Ahmad. "Functional Principal Component Analysis for Heterogeneous Survival Data." Doctoral dissertation, Bowling Green State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1713798310335436

    Chicago Manual of Style (17th edition)

bgsu1713798310335436
© 2024, all rights reserved.
This open access ETD is published by Bowling Green State University and OhioLINK.