Identifying Heterogeneous Effect Using Latent Supervised Clustering With Adaptive Fusion

Precision medicine is an important area of research with the goal of identifying the optimal treatment for each individual patient. In the literature, various methods are proposed to divide the population into subgroups according to the heterogeneous effects of individuals. In this article, a new ex...

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Veröffentlicht in:Journal of computational and graphical statistics Jg. 30; H. 1; S. 43 - 54
Hauptverfasser: Chen, Jingxiang, Tran-Dinh, Quoc, Kosorok, Michael R., Liu, Yufeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Taylor & Francis 2021
Taylor & Francis Ltd
Schlagworte:
Accelerated proximal gradient algorithm
Algorithms
Clustering
Clustering analysis
Convex clustering
Design parameters
Machine learning
Parameter estimation
Precision medicine
Subgroups
Subpopulation identification
Convex Clustering
Clustering Analysis
Subpopulation Identification
Machine Learning
Accelerated Proximal Gradient Algorithm
Precision Medicine
ISSN:1061-8600, 1537-2715
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Zusammenfassung:Precision medicine is an important area of research with the goal of identifying the optimal treatment for each individual patient. In the literature, various methods are proposed to divide the population into subgroups according to the heterogeneous effects of individuals. In this article, a new exploratory machine learning tool, named latent supervised clustering, is proposed to identify the heterogeneous subpopulations. In particular, we formulate the problem as a regression problem with subject specific coefficients, and use adaptive fusion to cluster the coefficients into subpopulations. This method has two main advantages. First, it relies on little prior knowledge and weak parametric assumptions on the underlying subpopulation structure. Second, it makes use of the outcome-predictor relationship, and hence can have competitive estimation and prediction accuracy. To estimate the parameters, we design a highly efficient accelerated proximal gradient algorithm which guarantees convergence at a competitive rate. Numerical studies show that the proposed method has competitive estimation and prediction accuracy, and can also produce interpretable clustering results for the underlying heterogeneous effect. Supplementary materials for this article are available online.
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ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2020.1763808