Date of Award

8-2014

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Dr. Joshua D. Naranjo

Second Advisor

Dr. Rajib Paul

Third Advisor

Dr. Jung Chao Wang

Fourth Advisor

Dr. Mark Schauer

Keywords

Survival analysis, odds ratio, logistical regression, hazard ratio, risk ratio, poisson regression

Abstract

Cox proportional hazards is the standard method for analyzing treatment efficacy when time-to-event data is available. In the absence of time-to-event, investigators may use logistic regression which only requires relative frequencies of events, or Poisson regression which requires only interval-summarized frequency tables of time-to-event. When event frequencies are used instead of time-to-events, does it always result in a loss in power?

We investigate the relative performance of the three methods. In particular, we compare the power of tests based on the respective effect-size estimates (1)hazard ratio (HR), (2)odds ratio (OR), and (3)risk ratio (RR). We use a variety of survival distributions and cut-off points representing length of study. We will show that the relative performance of OR against HR depends on the relative early-or-late separation of the two survival curves, and that OR and HR performed better than RR. We propose diagnostics based on the maximum separation to help investigators choose between OR and HR.

Access Setting

Dissertation-Open Access

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