Joint modeling approaches for clustered survival data with random cluster size

Author/Creator:

Liu, Shuling, author

Publication/Creation:

Ann Arbor : ProQuest Dissertations & Theses, 2015

Resource Type:

Book

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Additional/Related Title Information

Full Title:

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

Related Names

Additional Author/Creators:

Manatunga, Amita, degree supervisor
Emory University. Department of Biostatistics, degree granting institution

Subjects/Genre

Genre:

Electronic dissertations
Academic theses

Description/Summary

Summary:

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.
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.
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.

Language:

English

Language Note:

English

Physical Type/Description:

1 online resource (1 electronic resource (146 pages))

General Note:

Source of abstract: Dissertation Abstracts International, Volume: 77-04(E), Section: B.
Includes supplementary digital materials.
Advisors: Amita Manatunga ; Committee members: Robert Lyles; Michele Marcus; Limin Peng.

Local Note:

ProQuest digital dissertation copies of Emory dissertations may be downloaded free of charge by Emory faculty, students, and staff unless the author has chosen to embargo the work.

Additional Identifiers

Catalog ID (MMSID):

9936517611702486

ISBN:

9781339258881