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Gaskins, J.T.; Fuentes, C.; De la Cruz, R. |
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Title |
A Bayesian nonparametric model for classification of longitudinal profiles |
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2022 |
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Biostatistics |
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Biostatistics |
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Early Access |
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Bayesian nonparametric; Dirichlet process; Longitudinal data; Model-based classification |
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Across several medical fields, developing an approach for disease classification is an important challenge. The usual procedure is to fit a model for the longitudinal response in the healthy population, a different model for the longitudinal response in the diseased population, and then apply Bayes' theorem to obtain disease probabilities given the responses. Unfortunately, when substantial heterogeneity exists within each population, this type of Bayes classification may perform poorly. In this article, we develop a new approach by fitting a Bayesian nonparametric model for the joint outcome of disease status and longitudinal response, and then we perform classification through the clustering induced by the Dirichlet process. This approach is highly flexible and allows for multiple subpopulations of healthy, diseased, and possibly mixed membership. In addition, we introduce an Markov chain Monte Carlo sampling scheme that facilitates the assessment of the inference and prediction capabilities of our model. Finally, we demonstrate the method by predicting pregnancy outcomes using longitudinal profiles on the human chorionic gonadotropin beta subunit hormone levels in a sample of Chilean women being treated with assisted reproductive therapy. |
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1465-4644 |
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WOS:000756472200001 |
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UAI @ alexi.delcanto @ |
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1545 |
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Author |
Marquez, M.; Meza, C.; Lee, D.J.; De la Cruz, R. |
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Title |
Classification of longitudinal profiles using semi-parametric nonlinear mixed models with P-Splines and the SAEM algorithm |
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2023 |
Publication |
Statistics in Medicine |
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Stat. Med. |
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Early Access |
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Keywords |
longitudinal data; nonlinear mixed models; P-splines; SAEM algorithm; supervised classification |
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Abstract |
In this work, we propose an extension of a semiparametric nonlinear mixed-effects model for longitudinal data that incorporates more flexibility with penalized splines (P-splines) as smooth terms. The novelty of the proposed approach consists of the formulation of the model within the stochastic approximation version of the EM algorithm for maximum likelihood, the so-called SAEM algorithm. The proposed approach takes advantage of the formulation of a P-spline as a mixed-effects model and the use of the computational advantages of the existing software for the SAEM algorithm for the estimation of the random effects and the variance components. Additionally, we developed a supervised classification method for these non-linear mixed models using an adaptive importance sampling scheme. To illustrate our proposal, we consider two studies on pregnant women where two biomarkers are used as indicators of changes during pregnancy. In both studies, information about the women's pregnancy outcomes is known. Our proposal provides a unified framework for the classification of longitudinal profiles that may have important implications for the early detection and monitoring of pregnancy-related changes and contribute to improved maternal and fetal health outcomes. We show that the proposed models improve the analysis of this type of data compared to previous studies. These improvements are reflected both in the fit of the models and in the classification of the groups. |
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0277-6715 |
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WOS:001058478500001 |
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UAI @ alexi.delcanto @ |
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1878 |
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