Dissertation/Thesis Abstract

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Joint modeling of bivariate time to event data with semi-competing risk
by Liao, Ran, Ph.D., Indiana University - Purdue University Indianapolis, 2017, 148; 10254205
Abstract (Summary)

Survival analysis often encounters the situations of correlated multiple events including the same type of event observed from siblings or multiple events experienced by the same individual. In this dissertation, we focus on the joint modeling of bivariate time to event data with the estimation of the association parameters and also in the situation of a semi-competing risk.

This dissertation contains three related topics on bivariate time to event models. The first topic is on estimating the cross ratio which is an association parameter between bivariate survival functions. One advantage of using cross-ratio as a dependence measure is that it has an attractive hazard ratio interpretation by comparing two groups of interest. We compare the parametric, a two-stage semiparametric and a nonparametric approaches in simulation studies to evaluate the estimation performance among the three estimation approaches.

The second part is on semiparametric models of univariate time to event with a semi-competing risk. The third part is on semiparametric models of bivariate time to event with semi-competing risks. A frailty-based model framework was used to accommodate potential correlations among the multiple event times. We propose two estimation approaches. The first approach is a two stage semiparametric method where cumulative baseline hazards were estimated by nonparametric methods first and used in the likelihood function. The second approach is a penalized partial likelihood approach. Simulation studies were conducted to compare the estimation accuracy between the proposed approaches. Data from an elderly cohort were used to examine factors associated with times to multiple diseases and considering death as a semi-competing risk.

Indexing (document details)
Advisor: Gao, Sujuan
Commitee: Katz, Barry, Li, Shanshan, Zhang, Jianjun, Zhang, Ying
School: Indiana University - Purdue University Indianapolis
Department: Biostatistics
School Location: United States -- Indiana
Source: DAI-B 78/07(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Biostatistics, Mathematics, Statistics
Keywords: Copula, Cross ratio, Frailty model, Multivariate, Survival analysis
Publication Number: 10254205
ISBN: 9781369632804