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Hybrid Mixture Model for Subpopulation Identification

Author

Listed:
  • Hung-Chia Chen

    (U.S. Food and Drug Administration)

  • James J. Chen

    (U.S. Food and Drug Administration)

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.

Suggested Citation

  • Hung-Chia Chen & James J. Chen, 2016. "Hybrid Mixture Model for Subpopulation Identification," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 28-42, June.
    • Handle: RePEc:spr:stabio:v:8:y:2016:i:1:d:10.1007_s12561-015-9131-y
    DOI: 10.1007/s12561-015-9131-y
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    References listed on IDEAS

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