國立虎尾科技大學 |

Flexible Statistical Learning Methods for Survival Data : = Risk Prediction and Optimal Treatment Decision.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Flexible Statistical Learning Methods for Survival Data :/
其他題名:	Risk Prediction and Optimal Treatment Decision.
作者:	Geng, Yuan.
面頁冊數:	1 online resource (102 pages)
附註:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
Contained By:	Dissertation Abstracts International75-03B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9781303547089

Flexible Statistical Learning Methods for Survival Data : = Risk Prediction and Optimal Treatment Decision.
Geng, Yuan.

Flexible Statistical Learning Methods for Survival Data :Risk Prediction and Optimal Treatment Decision. - 1 online resource (102 pages)

Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2013.

Includes bibliographical references

In survival analysis, the major endpoint of interest is time-to-event data, which is usually subject to censoring. Among various problems in this area, we focus on two in this dissertation: survival risk prediction and optimal treatment decision.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781303547089Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Flexible Statistical Learning Methods for Survival Data : = Risk Prediction and Optimal Treatment Decision.
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245 1 0 $a Flexible Statistical Learning Methods for Survival Data : $b Risk Prediction and Optimal Treatment Decision.
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300 $a 1 online resource (102 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
500 $a Advisers: Wenbin Lu; Hao Helen Zhang.
502 $a Thesis (Ph.D.)--North Carolina State University, 2013.
504 $a Includes bibliographical references
520 $a In survival analysis, the major endpoint of interest is time-to-event data, which is usually subject to censoring. Among various problems in this area, we focus on two in this dissertation: survival risk prediction and optimal treatment decision.
520 $a In Chapter 2, we propose a new model-free machine learning method for risk classification and survival probability prediction, which plays an important role in patients' risk stratification, long-term diagnosis and treatment selection. The proposed method is based on weighted support vector machines (wSVMs) equipped with the inverse probability of censoring weighting (IPCW) technique. The new approach does not require any specific parametric or semiparametric model assumption, and is therefore robust. In addition, it is capable of capturing nonlinear covariate effects when a flexible kernel function is used. We demonstrate numerous simulation examples to show finite sample performance of the proposed method under different settings. Applications to a glioma tumor data and a breast cancer gene expression data are given to further illustrate the methodology in real data analysis.
520 $a In Chapter 3, we are interested in finding the best treatment rules that maximize patients' mean survival time. Due to patient's heterogeneity in response to treatments, great efforts have been devoted to developing optimal treatment regimes by integrating individuals' clinical and genetic information. A main challenge arises from the selection of important variables that can help to build reliable and interpretable optimal treatment regimes since the dimension of predictors may be high. We propose a robust loss-based estimation framework that can be easily coupled with shrinkage penalties for both estimation and variable selection. The asymptotic properties are studied for the proposed estimators for regression coefficients and the associated estimate of the restricted mean log survival time under the derived optimal treatment regimes. Simulations are conducted to assess the empirical performance of the proposed method for parameter estimation, variable selection and optimal treatment decision. An application to a survival data from an AIDS clinical trial is also given to illustrate the method.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a North Carolina State University. $b Statistics. $3 1184459
773 0 $t Dissertation Abstracts International $g 75-03B(E).
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