Date of Award

2021

Degree Type

Thesis

Degree Name

Master of Science in Statistics

Department

Computer Science and Statistics

First Advisor

Yichi Zhang

Abstract

A key characteristic that distinguishes survival analysis from other statistics fields is that survival data are usually censored or incomplete in some way. The event time is interval-censored when the exact event time is unknown and the event occurs within some interval of time.

The purposes of this study included: developed an easy-to-use code for interval-censored survival data with both fixed and time-dependent covariates; conducted extensive simulations to investigate the robustness of the interval-censored survival analysis with inaccurate time bounds and time-dependent covariates, particularly under noninformative censoring and informative censoring; conducted a real data analysis using the ACTG 175 data to investigate the robustness of findings under inaccurate time bounds.

The likelihood approach was used to draw inferences about the unknown parameters. The parameter estimates and standard errors were obtained; confidence intervals were constructed. The findings for simulations demonstrated that for both noninformative censoring and informative censoring, parameter estimates and coverage probability were more robust against deviations from the true time bounds for regression coefficients of the fixed and time-dependent covariates than for general hazard function parameters. The findings for real data analysis demonstrated that for both noninformative censoring and informative censoring, the estimates and p-values for the fixed and time-dependent covariates were robust against deviations from the true time bounds.

This study was the first to develop the code for interval-censored survival data with both fixed and time-dependent covariates, and was the first to investigate the robustness of the interval-censored survival analysis under inaccurate time bounds.

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