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Joint modeling approaches for cluste...

Liu, Shuling.

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Joint modeling approaches for clustered survival data with random cluster size.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Joint modeling approaches for clustered survival data with random cluster size./
Author:	Liu, Shuling.
Description:	146 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-04(E), Section: B.
Contained By:	Dissertation Abstracts International77-04B(E).
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3736896
ISBN:	9781339258881

Joint modeling approaches for clustered survival data with random cluster size.
Liu, Shuling.

Joint modeling approaches for clustered survival data with random cluster size. - 146 p.

Source: Dissertation Abstracts International, Volume: 77-04(E), Section: B.

Thesis (Ph.D.)--Emory University, 2015.

The first part of this dissertation focuses on the development of copula based joint modeling approaches for the clustered survival data with a random cluster size. We propose to adopt Clayton-Oakes model (Clayton, 1978; Oakes, 1989) for measurements within a cluster and the cluster size is modeled via a discrete survival model. The methods are motivated by the Mount Sinai Study of Women Office Workers (MSSWOW) where women were prospectively followed for one year for studying fertility. For each woman, menstrual cycle lengths (MCLs) are recorded until time-to-pregnancy (TTP) or the end of study.

ISBN: 9781339258881Subjects--Topical Terms:

1002712
Biostatistics.

Joint modeling approaches for clustered survival data with random cluster size.
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020 $a 9781339258881
035 $a (MiAaPQ)AAI3736896
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100 1 $a Liu, Shuling. $3 3191886
245 1 0 $a Joint modeling approaches for clustered survival data with random cluster size.
300 $a 146 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-04(E), Section: B.
500 $a Includes supplementary digital materials.
500 $a Adviser: Amita Manatunga.
502 $a Thesis (Ph.D.)--Emory University, 2015.
520 $a The first part of this dissertation focuses on the development of copula based joint modeling approaches for the clustered survival data with a random cluster size. We propose to adopt Clayton-Oakes model (Clayton, 1978; Oakes, 1989) for measurements within a cluster and the cluster size is modeled via a discrete survival model. The methods are motivated by the Mount Sinai Study of Women Office Workers (MSSWOW) where women were prospectively followed for one year for studying fertility. For each woman, menstrual cycle lengths (MCLs) are recorded until time-to-pregnancy (TTP) or the end of study.
520 $a We first consider specifying a parametric distribution as the marginal survival distribution in the Clayton-Oakes model and TTP is modeled using a grouped version of the usual continuous time Cox regression model (Scheike and Jensen, 1997). Second, we consider a semiparametric linear transformation model (Cheng et al., 1995) for the marginal distribution of the Clayton-Oakes model. We develop an EM algorithm to derive an approximate generalized maximum likelihood estimator. We also provide a computationally simple estimation procedure known as the "two-stage" approach. Asymptotic theory for the "two-stage" estimators is established. Simulation studies are conducted to evaluate the performance of the proposed joint model and estimation procedures. The proposed methods are also applied to the MSSWOW data.
520 $a In the second part of this dissertation, we consider the problem of testing whether a repeatedly measured quantitative biomarker is associated with a subsequent time-to-event process. We propose a nonparametric testing procedure to evaluate the null hypothesis by adopting a linear mixed model for repeated measures, but without imposing modeling assumptions on the time to event. The proposed test can utilize all the information provided by the random effects and is not sensitive to the model misspecification of the time-to-event process. We show that the proposed test statistic is asymptotically consistent and normally distributed under both null and alternative hypotheses. We demonstrate the validity of the new nonparametric test using simulation studies and compare the proposed method to a model-based score test. We finally apply the proposed method to a real data from epidemiological study to illustrate its practical utility.
590 $a School code: 0665.
650 4 $a Biostatistics. $3 1002712
690 $a 0308
710 2 $a Emory University. $b Biostatistics. $3 3168435
773 0 $t Dissertation Abstracts International $g 77-04B(E).
790 $a 0665
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3736896