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Scalable Methods for Big Time-to-Eve...

Kawaguchi, Eric Shinya.

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Scalable Methods for Big Time-to-Event Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scalable Methods for Big Time-to-Event Data./
Author:	Kawaguchi, Eric Shinya.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	159 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13896445
ISBN:	9781392233054

Scalable Methods for Big Time-to-Event Data.
Kawaguchi, Eric Shinya.

Scalable Methods for Big Time-to-Event Data. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 159 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2019.

This item must not be sold to any third party vendors.

Computational advancements and cost efficiency over the recent years have made big data readily available to researchers. In the biomedical and public health fields analyzing time-to-event data, where the outcome of interest is a time-to-event endpoint, is of particular interest. However, big time-to-event data poses many challenges to currently-available statistical methods due to the large number of covariates and/or observations one can observe. In this dissertation we propose scalable sparse regression methods for both big right-censored and competing risks time-to-event data. We extend the recently-introduced broken adaptive ridge (BAR) regression procedure to both the Cox (1972) proportional hazards for right-censored data and the Fine and Gray (1999) proportional subdistribution hazards model for competing risks data, establish its large-sample properties under diverging dimension, and develop computational software that is scalable to big time-to-event data.

ISBN: 9781392233054Subjects--Topical Terms:

1002712
Biostatistics.

Scalable Methods for Big Time-to-Event Data.
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245 1 0 $a Scalable Methods for Big Time-to-Event Data.
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300 $a 159 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Li, Gang.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Computational advancements and cost efficiency over the recent years have made big data readily available to researchers. In the biomedical and public health fields analyzing time-to-event data, where the outcome of interest is a time-to-event endpoint, is of particular interest. However, big time-to-event data poses many challenges to currently-available statistical methods due to the large number of covariates and/or observations one can observe. In this dissertation we propose scalable sparse regression methods for both big right-censored and competing risks time-to-event data. We extend the recently-introduced broken adaptive ridge (BAR) regression procedure to both the Cox (1972) proportional hazards for right-censored data and the Fine and Gray (1999) proportional subdistribution hazards model for competing risks data, establish its large-sample properties under diverging dimension, and develop computational software that is scalable to big time-to-event data.
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