Date of Award

1-1-2016

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Epidemiology and Biostatistics

Program

Biostatistics

Content Description

1 online resource (iii, 148 pages) : illustrations (some color)

Dissertation/Thesis Chair

A. G. DiRienzo

Committee Members

Recai M. Yucel, Yuchi Young

Keywords

Survival analysis (Biometry), Proportional hazards models, Biometry

Subject Categories

Biostatistics

Abstract

There are three papers each on a different topic in this thesis. The first paper proposes a new objective methodology to estimate any subject specific survival distribution with potential time-varying effect by adjusting approximated polynomial censored survival function with estimated censoring distribution under three different assumptions: uniform censoring, independent censoring and non-informative censoring. The coefficients of the polynomial censored survival function and underlying censoring probability are estimated at each event or censoring time point across the study time frame, which naturally accommodates potential non-proportional hazards along with time-varying effect. An extensive simulation study indicates that the proposed methods usually perform better than Cox proportional hazard (PH) model with few exceptions according to the true survival distribution generated from empirical cumulative distribution function. The well-known Mayo Clinic primary biliary cirrhosis dataset is used to illustrate those proposals compared with Cox PH model and a recent kernel-weighted smoothing estimator.

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