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Hybrid Mixture Model for Subpopulation Identification

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Abstract

Personalized medicine aims to identify those patients who have good or poor prognosis for overall disease outcomes or therapeutic efficacy for a specific treatment. A well-established approach is to identify a set of biomarkers using statistical methods with a classification algorithm to identify patient subgroups for treatment selection. However, there are potential false positives and false negatives in classification resulting in incorrect patient treatment assignment. In this paper, we propose a hybrid mixture model taking uncertainty in class labels into consideration, where the class labels are modeled by a Bernoulli random variable. An EM algorithm was developed to estimate the model parameters, and a parametric bootstrap method was used to test the significance of the predictive variables that were associated with subgroup memberships. Simulation experiments showed that the proposed method averagely had higher accuracy in identifying the subpopulations than the Naïve Bayes classifier and logistic regression. A breast cancer dataset was analyzed to illustrate the proposed hybrid mixture model.

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Authors and Affiliations

  1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA

    Hung-Chia Chen & James J. Chen

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  1. Hung-Chia Chen
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  2. James J. Chen
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Correspondence to James J. Chen.

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The views presented in this paper are those of the authors and do not necessarily represent those of the U.S. Food and Drug Administration.

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Chen, HC., Chen, J.J. Hybrid Mixture Model for Subpopulation Identification. Stat Biosci 8, 28–42 (2016). https://doi.org/10.1007/s12561-015-9131-y

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