Date of Award

1-1-2013

Document Type

Open Access Thesis

Department

Epidemiology and Biostatistics

Sub-Department

Biostatistics

First Advisor

Jiajia Zhang

Abstract

The assessment of overall homogeneity of time-to-event curves is a key element in survival analysis in biomedical research. The currently commonly used testing methods, e.g. log-rank test, Wilcoxon test, and Kolmogorov-Smirnov test, may have a significant loss of statistical testing power under certain circumstances. In this thesis we replicate a testing method (Lin & Xu, 2009) that is robust for the comparison of the overall homogeneity of survival curves based on the absolute difference of the area under the survival curves using normal approximation by Greenwood's formula, and propose a new weight component to their test statistic. The weight component is added to Lin and Xu's test statistic to better fit the data at hand (i.e. emphasizing more weight on earlier data). Monte Carlo simulations are conducted to investigate the performance of the new testing method compared against the log-rank, Wilcoxon, Kolmogorov-Smirnov, and Lin & Xu's tests under a variety of circumstances. The proposed new weighted method has robust performance compared to the common test statistics, with greater power to detect the overall differences than the log-rank, Wilcoxon, Kolmogorov-Smirnov, and Lin & Xu's (2009) tests in many scenarios resulting from the simulations. Furthermore, the applicability of the new testing approach is illustrated in a real data example from a Leukemia analysis trial.

Included in

Biostatistics Commons

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