東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Novel Statistical Methods for Causal...

Morenz, Eric.

Linked to FindBook

Google Book

Amazon

博客來

Novel Statistical Methods for Causal Inference Based on Truncated and Censored Time-to-Event Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Novel Statistical Methods for Causal Inference Based on Truncated and Censored Time-to-Event Data./
Author:	Morenz, Eric.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	187 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
Subject:	Biostatistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30817756
ISBN:	9798381409352

Novel Statistical Methods for Causal Inference Based on Truncated and Censored Time-to-Event Data.
Morenz, Eric.

Novel Statistical Methods for Causal Inference Based on Truncated and Censored Time-to-Event Data. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 187 p.

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

Thesis (Ph.D.)--University of Washington, 2023.

In this dissertation, we discuss two settings in which association parameters based on traditional models can be challenging to interpret causally, even when exposure level is randomized. The first setting pertains to human autopsy studies, in which a time-varying outcome is only measured at death. When the exposure affects survival, the distribution of outcome measurement times differs across exposure levels, and can produce misleading comparisons. We propose to study the causal effect of the exposure on the outcome process using a contrast inspired by the mediation literature and that we refer to as the natural-time direct effect. As a first step, we derive nonparametric debiased machine learning approaches for inference on survival integrals using left-truncated right-censored data. We then use these methods to assess the effect of the APOE-4 gene on autopsy measures of Braak staging using data from the Adult Changes in Thought study. In the second setting, a point-exposure of varying doses occurs at a random time, and we wish to characterize the (possibly exposure time-specific) effect of dose on survival. In this context, standard survival models are often not amenable to interpretably addressing the scientific question at hand. As an alternative, we propose to use an accelerated residual failure time model. We develop methods for inference on model parameters indexing a parametric accelerated residual failure time model using left-truncated right-censored data, and provide a causal interpretation under natural causal conditions. Using data from the Life Span Study, a long-term prospective cohort study of survivors of the atomic bombings of Hiroshima and Nagasaki, we use these methods to study how a point-exposure to radiation of differing doses affects survival.

ISBN: 9798381409352Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Human autopsy

Novel Statistical Methods for Causal Inference Based on Truncated and Censored Time-to-Event Data.
LDR:02905nmm a2200361 4500 001 2399292
005 20240909103809.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798381409352
035 $a (MiAaPQ)AAI30817756
035 $a AAI30817756
040 $a MiAaPQ $c MiAaPQ
100 1 $a Morenz, Eric. $3 3769263
245 1 0 $a Novel Statistical Methods for Causal Inference Based on Truncated and Censored Time-to-Event Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 187 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
500 $a Advisor: Carone, Marco.
502 $a Thesis (Ph.D.)--University of Washington, 2023.
520 $a In this dissertation, we discuss two settings in which association parameters based on traditional models can be challenging to interpret causally, even when exposure level is randomized. The first setting pertains to human autopsy studies, in which a time-varying outcome is only measured at death. When the exposure affects survival, the distribution of outcome measurement times differs across exposure levels, and can produce misleading comparisons. We propose to study the causal effect of the exposure on the outcome process using a contrast inspired by the mediation literature and that we refer to as the natural-time direct effect. As a first step, we derive nonparametric debiased machine learning approaches for inference on survival integrals using left-truncated right-censored data. We then use these methods to assess the effect of the APOE-4 gene on autopsy measures of Braak staging using data from the Adult Changes in Thought study. In the second setting, a point-exposure of varying doses occurs at a random time, and we wish to characterize the (possibly exposure time-specific) effect of dose on survival. In this context, standard survival models are often not amenable to interpretably addressing the scientific question at hand. As an alternative, we propose to use an accelerated residual failure time model. We develop methods for inference on model parameters indexing a parametric accelerated residual failure time model using left-truncated right-censored data, and provide a causal interpretation under natural causal conditions. Using data from the Life Span Study, a long-term prospective cohort study of survivors of the atomic bombings of Hiroshima and Nagasaki, we use these methods to study how a point-exposure to radiation of differing doses affects survival.
590 $a School code: 0250.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
653 $a Human autopsy
653 $a Machine learning
653 $a Parameters
653 $a Braak staging
690 $a 0308
690 $a 0463
710 2 $a University of Washington. $b Biostatistics - Public Health. $3 3180015
773 0 $t Dissertations Abstracts International $g 85-07B.
790 $a 0250
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30817756