95
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Dynamic Survival Prediction Using Sparse Longitudinal Images via Multi-Dimensional Functional Principal Component Analysis

, , , & ORCID Icon
Received 13 Apr 2023, Accepted 16 Mar 2024, Published online: 23 May 2024

References

  • Chiou, J. M., Chen, Y. T., and Yang, Y. F. (2014), “Multivariate Functional Principal Component Analysis: A Normalization Approach,” Statistica Sinica, 24, 1571–1596. DOI: 10.5705/ss.2013.305.
  • Chen, L. H., and Jiang, C. R. (2017), “Multi-Dimensional Functional Principal Component Analysis,” Statistics and Computing, 27, 1181–1192. DOI: 10.1007/s11222-016-9679-5.
  • Consagra, W. (2022), “Methods for Multidimensional Functional Data Analysis in Modern Neuroimaging,” Doctoral Dissertation, University of Rochester, Rochester, NY.
  • Happ, C., and Greven, S. (2018), “Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains,” Journal of the American Statistical Association, 113, 649–659. DOI: 10.1080/01621459.2016.1273115.
  • Hasenstab, K., Scheffler, A., Telesca, D., Sugar, C. A., Jeste, S., DiStefano, C., and Senturk, D. (2017), “A Multi-Dimensional Functional Principal Components Analysis of EEG Data,” Biometrics, 73, 999–1009. DOI: 10.1111/biom.12635.
  • Jiang, S., Xie, Y., and Colditz, G. (2020), “Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates,” Journal of the Royal Statistical Society, Series C, 70, 66–79. DOI: 10.1111/rssc.12449.
  • Kong, D., Ibrahim, J. G., Lee, E., and Zhu, H. (2018), “FLCRM: Functional Linear Cox Regression Model,” Biometrics, 74, 109–117. DOI: 10.1111/biom.12748.
  • Lawson, C. L., and Hanson, R. J. (1974), Solving Least Squares Problems, Englewood Cliffs: Prentice Hall.
  • Li, K., and Luo, S. (2019a), “Dynamic Prediction of Alzheimer’s Disease Progression Using Features of Multiple Longitudinal Outcomes and Time-to-Event Data,” Statistics in Medicine, 38, 4804–4818. DOI: 10.1002/sim.8334.
  • Li, K., and Luo, S. (2019b), “Dynamic Predictions in Bayesian Functional Joint Models for Longitudinal and Time-to-Event Data: An Application to Alzheimer’s Disease,” Statistical Methods in Medical Research, 28, 327–342. DOI: 10.1177/0962280217722177.
  • Li, K., and Luo, S. (2019c), “Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data,” Computatoinal Statistics and Data Analysis, 129, 14–29.
  • Li, C., Xiao, L., and Luo, S. (2020), “Fast Covariance Estimation for Multivariate Sparse Functional Data,” Stat, 9, e245. DOI: 10.1002/sta4.245.
  • Lin, J., Li, K., and Luo, S. (2021), “Functional Survival Forests for Multivariate Longitudinal Outcomes: Dynamic Prediction of Alzheimer’s Disease Progression,” Statistical Methods in Medical Research, 30, 99–111. DOI: 10.1177/0962280220941532.
  • Lin, Z., Wang, L., and Cao, J. (2016), “Interpretable Functional Principal Component Analysis,” Biometrics, 72, 846–854. DOI: 10.1111/biom.12457.
  • Ma, D., Popuri, K., Bhalla, M., Sangha, O., Lu, D., Cao, J., Jacova, C., Wang, L., and Beg, M. F. (2018), “Quantitative Assessment of Field Strength, Total Intracranial Volume, Sex, and Age Effects on the Goodness of Harmonization for Volumetric Analysis on the ADNI Database,” Human Brain Mapping, 40, 125–136. DOI: 10.1002/hbm.24463.
  • Nie, Y., Wang, L., Liu, B., and Cao, J. (2018), “Supervised Functional Principal Component Analysis,” Statistics and Computing, 28, 713–723. DOI: 10.1007/s11222-017-9758-2.
  • Nie, Y., and Cao, J. (2020), “Sparse Functional Principal Component Analysis in a New Regression Framework,” Computational Statistics & Data Analysis, 152, 107016. DOI: 10.1016/j.csda.2020.107016.
  • Nie, Y., Wang, L., and Cao, J. (2022), “Recovering the Underlying Trajectory from Sparse and Irregular Longitudinal Data,” Canadian Journal of Statistics, 50, 122–141. DOI: 10.1002/cjs.11677.
  • Popuri, K., Ma, D., Wang, L., and Beg, M. F. (2020), “Use Machine Learning to Quantify Structural MRI Neurodegeneration Patterns of Alzheimer’s Disease into Dementia Score: Independent Validation on 8834 Images from ADNI, AIBL, OASIS and MIRIAD Databases,” Human Brain Mapping, 41, 4127–4147. DOI: 10.1002/hbm.25115.
  • Ramsay, J. O., and Dalzell, C. (1991), “Some Tools for Functional Data Analysis,” Journal of the Royal Statistical Society, Series B, 53, 539–572. DOI: 10.1111/j.2517-6161.1991.tb01844.x.
  • Sang, P., Wang, L., and Cao, J. (2017), “Parametric Functional Principal Component Analysis,” Biometrics, 73, 802–810. DOI: 10.1111/biom.12641.
  • Shi, H., Dong, J., Wang, L., and Cao, J. (2021), “Functional Principal Component Analysis for Longitudinal Data with Informative Dropout,” Statistics in Medicine, 40, 712–724. DOI: 10.1002/sim.8798.
  • Shi, H., Yang, Y., Wang, L., Ma, D., Beg, M. F., Pei, J., and Cao, J. (2022), “Two-Dimensional Functional Orthogonal Approximation Method for Image Feature Extraction,” Journal of Computational and Graphical Statistics, 31, 1127–1140. DOI: 10.1080/10618600.2022.2035738.
  • Silverman, B. W. (1996), “Smoothed Functional Principal Components Analysis by Choice of Norm,” The Annals of Statistics, 24, 1–24. DOI: 10.1214/aos/1033066196.
  • Welsh, A. H., Lin, X., and Carroll, R. J. (2002), “Marginal Longitudinal Nonparametric Regression,” Journal of the American Statistical Association, 97, 482–493. DOI: 10.1198/016214502760047014.
  • Yao, F., Müller, H. G., and Wang, J. L. (2005), “Functional Data Analysis for Sparse Longitudinal Data,” Journal of the American Statistical Association, 100, 577–590. DOI: 10.1198/016214504000001745.
  • Zou, H., Li, K., Zeng, D., and Luo, S. (2021), “Bayesian Inference and Dynamic Prediction of Multivariate Joint Model with Functional Data: An Application to Alzheimer’s Disease,” Statisics in Medicine, 40, 6855–6872.
  • Zou, H., Xiao, L., Zeng, D., Luo, S. (2023), “Multivariate Functional Mixed Model with MRI Data: An Application to Alzheimer’s Disease,” Statistics in Medicine, 42, 1492–1511. DOI: 10.1002/sim.9683.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.