Author

Sydney Smith

Date of Award

Summer 2020

Document Type

Open Access Thesis

Department

Epidemiology and Biostatistics

First Advisor

Andrew Ortaglia

Abstract

The Cox proportional hazards model is the most common regression technique for survival analysis. However, the proportional hazards assumption restricts it’s use to a limited group of multiplicative models. Laplace regression is a flexible quantile regression technique for censored observations that is appropriate in a wider variety of applications as compared to the Cox proportional hazards model. Instead of estimating a hazard ratio, Laplace regression which is free from a proportionality assumption, can be used to estimate many adjusted percentiles of survival time allowing for a more complete description of the association of interest. This paper compares the performance of these two analytic techniques with multiple simulation studies and an application to a dataset of radiation levels and survival times of employees at U.S. government facilities.

Available for download on Monday, August 15, 2022

Included in

Biostatistics Commons

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