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New Model-Based and Deep Learning Methods for Survival Data with or Without Competing Risks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	New Model-Based and Deep Learning Methods for Survival Data with or Without Competing Risks./
作者:	Jia, Yichen.
面頁冊數:	1 online resource (93 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Probability. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29281169click for full text (PQDT)
ISBN:	9798845433183

New Model-Based and Deep Learning Methods for Survival Data with or Without Competing Risks.
Jia, Yichen.

New Model-Based and Deep Learning Methods for Survival Data with or Without Competing Risks. - 1 online resource (93 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2022.

Includes bibliographical references

Survival data (or time-to-event data) is a special type of data that focus on the time until occurrence of an event of interest. Traditional statistical methods have been based on the survival function or hazard function. This dissertation proposes inference and prediction models for survival data that focus on event time itself and its various quantities.In the first part, a quantile regression model is proposed to associate the inactivity time, a new summary measure for survival data, with covariates under competing risks. Asymptotic properties were derived for the regression coefficient estimators and associated test statistics. Simulation results show that my proposed method works well under the assumed finite sample settings. The proposed method is then illustrated with a real dataset from a breast cancer study.In the second part, a deep learning method for quantile regression, DeepQuantreg, is developed to predict conditional quantile survival time. The Huber check function was adopted in the loss function with inverse probability weights to adjust for censoring. Sim- ulation studies were performed to generate nonlinear censored survival data and compare the deep learning method with the traditional linear quantile regression and nonparametric quantile regression. The proposed method is illustrated with two publicly available breast cancer data sets with gene signatures.In the third part, a deep learning method with an innovative loss function, DeepCENT, is proposed to directly predict survival time. The newly proposed loss function combines the mean square error and the concordance index, which not only consider the prediction accu- racy but also the discriminative performance. Moreover, DeepCENT can handle competing risks. The validity and advantage of DeepCENT were evaluated using simulation studies and illustrated with three publicly available cancer data sets.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798845433183Subjects--Topical Terms:

518898
Probability.
Index Terms--Genre/Form:

542853
Electronic books.

New Model-Based and Deep Learning Methods for Survival Data with or Without Competing Risks.
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520 $a Survival data (or time-to-event data) is a special type of data that focus on the time until occurrence of an event of interest. Traditional statistical methods have been based on the survival function or hazard function. This dissertation proposes inference and prediction models for survival data that focus on event time itself and its various quantities.In the first part, a quantile regression model is proposed to associate the inactivity time, a new summary measure for survival data, with covariates under competing risks. Asymptotic properties were derived for the regression coefficient estimators and associated test statistics. Simulation results show that my proposed method works well under the assumed finite sample settings. The proposed method is then illustrated with a real dataset from a breast cancer study.In the second part, a deep learning method for quantile regression, DeepQuantreg, is developed to predict conditional quantile survival time. The Huber check function was adopted in the loss function with inverse probability weights to adjust for censoring. Sim- ulation studies were performed to generate nonlinear censored survival data and compare the deep learning method with the traditional linear quantile regression and nonparametric quantile regression. The proposed method is illustrated with two publicly available breast cancer data sets with gene signatures.In the third part, a deep learning method with an innovative loss function, DeepCENT, is proposed to directly predict survival time. The newly proposed loss function combines the mean square error and the concordance index, which not only consider the prediction accu- racy but also the discriminative performance. Moreover, DeepCENT can handle competing risks. The validity and advantage of DeepCENT were evaluated using simulation studies and illustrated with three publicly available cancer data sets.
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