Functional Multivariable Logistic Regression with an Application to HIV Viral Suppression Prediction
Document Type
Article
Abstract
Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudinal binary process and continuous process simultaneously. Specifically, the longitudinal measurements for either binary or continuous variables are modeled by functional principal components analysis, and their corresponding functional principal component scores are used to build a logistic regression model for prediction. The longitudinal binary data are linked to underlying Gaussian processes. The estimation is done using penalized spline for the longitudinal continuous and binary data. Group-lasso is used to select longitudinal processes, and the multivariate functional principal components analysis is proposed to revise functional principal component scores with the correlation. The method is evaluated via comprehensive simulation studies and then applied to predict viral suppression using EHR data for people living with HIV in South Carolina.
Digital Object Identifier (DOI)
Publication Info
Published in Biometrical Journal, Volume 66, Issue 5, 2024.
Rights
© 2024 The Author(s). Biometrical Journal published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
APA Citation
Guo, S., Zhang, J., Wu, Y., McLain, A. C., Hardin, J. W., Bankole Olatosi, & Li, X. (2024). Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction. Biometrical Journal, 66(5).https://doi.org/10.1002/bimj.202300081