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Title: Analysis of Models for Epidemiologic and Survival Data
Authors: Suntornchost, Jiraphan
Advisors: Slud, Eric V
Department/Program: Mathematical Statistics
Type: Dissertation
Sponsors: Digital Repository at the University of Maryland
University of Maryland (College Park, Md.)
Subjects: Statistics
Keywords: Epidemiology
Issue Date: 2012
Abstract: Mortality statistics are useful tools for public-health statisticians, actuaries and policy makers to study health status of populations in communities and to make plans in health care systems. Several statistical models and methods of parameter estimation have been proposed. In this thesis, we review some benchmark mortality models and propose three alternative statistical models for both epidemiologic data and survival data. For epidemiologic data, we propose two statistical models, a Smoothed Segmented Lee-Carter model and a Smoothed Segmented Poisson Log-bilinear model. The models are modifications of the Lee-Carter (1992) model which combine an age segmented Lee-Carter parameterization with spline smoothed period effects within each age segment. With different period effects across age groups, the two models are fitted by maximizing respectively a penalized least squares criterion and a penalized Poisson likelihood. The new methods are applied to the 1971-2006 public-use mortality data sets released by the National Center for Health Statistics (NCHS). Mortality rates for three leading causes of death, heart diseases, cancer and accidents, are studied. For survival data, we propose a phase type model having features of mixtures, multiple stages or hits and a trapping state. Two parameter estimation techniques studied are a direct numerical method and an EM algorithm. Since phase type model parameters are known to be difficult to estimate, we study in detail the performance of our parameter estimation techniques by reference to the Fisher Information matrix. An alternative way to produce a Fisher Information matrix for an EM parameter estimation is also provided. The proposed model and the best available parameter estimation techniques are applied to a large SEER 1992-2002 breast cancer dataset.
Appears in Collections:Mathematics Theses and Dissertations
UMD Theses and Dissertations

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