Semiparametric mixed-effect least squares support vector machine for analyzing pharmacokinetic and pharmacodynamic data

  • Authors:

  • Kyung Ha Seok;Jooyong Shim;Daehyeon Cho;Gyu-Jeong Noh;Changha Hwang

  • Affiliations:

  • Institute of Statistical Information, Department of Data Science, Inje University, Kyungnam 621-749, South Korea;Institute of Statistical Information, Department of Data Science, Inje University, Kyungnam 621-749, South Korea;Institute of Statistical Information, Department of Data Science, Inje University, Kyungnam 621-749, South Korea;Department of Anesthesiology and Pain Medicine, Asan Medical Center, College of Medicine, University of Ulsan, Seoul 138-736, South Korea and Department of Clinical Pharmacology and Therapeutics, ...;Department of Statistics, Dankook University, Gyeonggido 448-160, South Korea

  • Venue:
  • Neurocomputing
  • Year:
  • 2011

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Abstract

In this paper we propose a semiparametric mixed-effect least squares support vector machine (LS-SVM) regression model for the analysis of pharmacokinetic (PK) and pharmacodynamic (PD) data. We also develop the generalized cross-validation (GCV) method for choosing the hyperparameters which affect the performance of the proposed LS-SVM. The performance of the proposed LS-SVM is compared with those of NONMEM and the regular semiparametric LS-SVM via four measures, which are mean squared error (MSE), mean absolute error (MAE), mean relative absolute error (MRAE) and mean relative prediction error (MRPE). Through paired-t test statistic we find that the absolute values of four measures of the proposed LS-SVM are significantly smaller than those of NONMEM for PK and PD data. We also investigate the coefficient of determinations R^2's of predicted and observed values. The R^2's of NONMEM are 0.66 and 0.59 for PK and PD data, respectively, while the R^2's of the proposed LS-SVM are 0.94 and 0.96. Through cross validation technique we also find that the proposed LS-SVM shows better generalization performance than the regular semiparametric LS-SVM for PK and PD data. These facts indicate that the proposed LS-SVM is an appealing tool for analyzing PK and PD data.