In some large clinical studies, it may be impractical to give physical examinations to every subject at his/her last monitoring
time in order to diagnose the occurrence of an event of interest. This challenge creates survival data with missing censoring indicators
where the probability of missing may depend on time of last monitoring. We present a fully Bayesian semi-parametric method for such survival
data to estimate regression parameters of Cox's proportional hazards model [Cox, 1972]. Simulation studies show that our method performs better than competing methods. We apply the proposed method to data from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study. Clinical studies often include interval censored data. We present a method for the simulation of interval censored data based on Poisson processes. We show that our method gives simulated data that fulfills the assumption of independent interval censoring, and is more computationally efficient that other methods used for simulating interval censored data. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2018. / July 10, 2018. / Includes bibliographical references. / Debajyoti Sinha, Professor Co-Directing Dissertation; Naomi Brownstein, Professor Co-Directing Dissertation; Richard Nowakowski, University Representative; Elizabeth Slate, Committee Member; Antonio Linero, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_647194 |
| Contributors | Bunn, Veronica (author), Sinha, Debajyoti (professor co-directing dissertation), Brownstein, Naomi Chana (professor co-directing dissertation), Slate, Elizabeth H. (committee member), Linero, Antonio Ricardo (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Statistics (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (59 pages), computer, application/pdf |
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