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Tests and Classifications in Adaptiv...

Chen, Qiusheng.

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Tests and Classifications in Adaptive Designs with Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Tests and Classifications in Adaptive Designs with Applications./
Author:	Chen, Qiusheng.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	104 p.
Notes:	Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Contained By:	Dissertation Abstracts International80-01B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10745200
ISBN:	9780438305540

Tests and Classifications in Adaptive Designs with Applications.
Chen, Qiusheng.

Tests and Classifications in Adaptive Designs with Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 104 p.

Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.

Thesis (Ph.D.)--The Florida State University, 2018.

Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials. In this article, we evaluate four test methods for biomarker identification: a model-based identification method, the popular t-test, the nonparametric Wilcoxon Rank Sum test, and the Least Absolute Shrinkage and Selection Operator (Lasso) method. For selecting the best classification methods in Stage 2 of an adaptive design, we examine classification methods including the recently developed machine learning approaches such as Random Forest, Lasso and Elastic-Net Regularized Generalized Linear Models (Glmnet), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Statistical simulations are carried out in our study to assess the performance of biomarker identification methods and the classification methods. The best identification method and the classification technique will be selected based on the True Positive Rate (TPR,also called Sensitivity) and the True Negative Rate (TNR,also called Specificity). The optimal test method for gene identification and classification method for patient grouping will be applied to the Adaptive Signature Design (ASD) for the purpose of evaluating the performance of ASD in different situations, including simulated data and a real data set for breast cancer patients.

ISBN: 9780438305540Subjects--Topical Terms:

517247
Statistics.

Tests and Classifications in Adaptive Designs with Applications.
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520 $a Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials. In this article, we evaluate four test methods for biomarker identification: a model-based identification method, the popular t-test, the nonparametric Wilcoxon Rank Sum test, and the Least Absolute Shrinkage and Selection Operator (Lasso) method. For selecting the best classification methods in Stage 2 of an adaptive design, we examine classification methods including the recently developed machine learning approaches such as Random Forest, Lasso and Elastic-Net Regularized Generalized Linear Models (Glmnet), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Statistical simulations are carried out in our study to assess the performance of biomarker identification methods and the classification methods. The best identification method and the classification technique will be selected based on the True Positive Rate (TPR,also called Sensitivity) and the True Negative Rate (TNR,also called Specificity). The optimal test method for gene identification and classification method for patient grouping will be applied to the Adaptive Signature Design (ASD) for the purpose of evaluating the performance of ASD in different situations, including simulated data and a real data set for breast cancer patients.
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