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Causal Mediation Analysis with failu...

Zheng, Cheng.

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Causal Mediation Analysis with failure time outcome and error-prone longitudinal covariate.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Causal Mediation Analysis with failure time outcome and error-prone longitudinal covariate./
Author:	Zheng, Cheng.
Description:	139 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-12(E), Section: B.
Contained By:	Dissertation Abstracts International75-12B(E).
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3632008
ISBN:	9781321112313

Causal Mediation Analysis with failure time outcome and error-prone longitudinal covariate.
Zheng, Cheng.

Causal Mediation Analysis with failure time outcome and error-prone longitudinal covariate. - 139 p.

Source: Dissertation Abstracts International, Volume: 75-12(E), Section: B.

Thesis (Ph.D.)--University of Washington, 2014.

Mediation analyses are important for understanding the biological mechanisms whereby a treatment/exposure influences an outcome of interest. For example, one may be interested in whether body fat accumulation mediates an association of certain dietary patterns with cancer risk. Similarly mediation analyses may aim to achieve an understanding of which elements of a multi-faceted dietary modification intervention were most influential in affecting disease incidence. Several challenges occur in mediation analysis: (1) the longitudinal and observational nature of the dietary variables and BMI/weight; (2) the measurement error in dietary variables which are often assessed using food frequency questionnaires; (3) control of measured/unmeasured confounders. In this dissertation, we proposed a general potential outcome framework for causal mediation analysis with failure time outcome and longitudinal mediator/exposure with measurement error. We proposed a method to correct for the systematic bias in longitudinal self-reported dietary data and use the calibrated data to estimate parameters in the survival model. We also proposed a robust estimator of key survival model parameters that can accommodate the existence of certain types of unmeasured confounders. We studied the performance of regression calibration methods for multiple choices of survival models numerically. We analyzed some important epidemiologic data and provided scientific information on the interplay between dietary exposures, physical activity and BMI in relation to site-specific cancer and other chronic diseases.

ISBN: 9781321112313Subjects--Topical Terms:

1002712
Biostatistics.

Causal Mediation Analysis with failure time outcome and error-prone longitudinal covariate.
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020 $a 9781321112313
035 $a (MiAaPQ)AAI3632008
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100 1 $a Zheng, Cheng. $3 3180016
245 1 0 $a Causal Mediation Analysis with failure time outcome and error-prone longitudinal covariate.
300 $a 139 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-12(E), Section: B.
500 $a Adviser: Ross L. Prentice.
502 $a Thesis (Ph.D.)--University of Washington, 2014.
520 $a Mediation analyses are important for understanding the biological mechanisms whereby a treatment/exposure influences an outcome of interest. For example, one may be interested in whether body fat accumulation mediates an association of certain dietary patterns with cancer risk. Similarly mediation analyses may aim to achieve an understanding of which elements of a multi-faceted dietary modification intervention were most influential in affecting disease incidence. Several challenges occur in mediation analysis: (1) the longitudinal and observational nature of the dietary variables and BMI/weight; (2) the measurement error in dietary variables which are often assessed using food frequency questionnaires; (3) control of measured/unmeasured confounders. In this dissertation, we proposed a general potential outcome framework for causal mediation analysis with failure time outcome and longitudinal mediator/exposure with measurement error. We proposed a method to correct for the systematic bias in longitudinal self-reported dietary data and use the calibrated data to estimate parameters in the survival model. We also proposed a robust estimator of key survival model parameters that can accommodate the existence of certain types of unmeasured confounders. We studied the performance of regression calibration methods for multiple choices of survival models numerically. We analyzed some important epidemiologic data and provided scientific information on the interplay between dietary exposures, physical activity and BMI in relation to site-specific cancer and other chronic diseases.
590 $a School code: 0250.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
650 4 $a Epidemiology. $3 568544
690 $a 0308
690 $a 0463
690 $a 0766
710 2 $a University of Washington. $b Biostatistics - Public Health. $3 3180015
773 0 $t Dissertation Abstracts International $g 75-12B(E).
790 $a 0250
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3632008