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Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

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  • Rong Liu
  • Xi Li
  • Wei Zhang
  • Hong-Hao Zhou

Abstract

Objective: Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods: MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results: BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values

Suggested Citation

  • Rong Liu & Xi Li & Wei Zhang & Hong-Hao Zhou, 2015. "Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-11, August.
  • Handle: RePEc:plo:pone00:0135784
    DOI: 10.1371/journal.pone.0135784
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    Cited by:

    1. Zhiyuan Ma & Ping Wang & Zehui Gao & Ruobing Wang & Koroush Khalighi, 2018. "Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-12, October.
    2. Fatemeh Tajik & Mingzheng Wang & Xiaohui Zhang & Jie Han, 2020. "Evaluation of the impact of body mass index on venous thromboembolism risk factors," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.

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