Nonparametric and Parametric Survival Analysis of Censored Data with Possible Violation of Method Assumptions

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Guolin Zhao (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site:
Kirsten Doehler

Abstract: Estimating survival functions has interested statisticians for numerous years. A survival function gives information on the probability of a time-to-event of interest. Research in the area of survival analysis has increased greatly over the last several decades because of its large usage in areas related to biostatistics and the pharmaceutical industry. Among the methods which estimate the survival function, several are widely used and available in popular statistical software programs. One purpose of this research is to compare the efficiency between competing estimators of the survival function. Results are given for simulations which use nonparametric and parametric estimation methods on censored data. The simulated data sets have right-, left-, or interval-censored time points. Comparisons are done on various types of data to see which survival function estimation methods are more suitable. We consider scenarios where distributional assumptions or censoring type assumptions are violated. Another goal of this research is to examine the effects of these incorrect assumptions.

Additional Information

Language: English
Date: 2008
Nonparametric, Parametric, Survival Analysis, Censored Data, Method, survival functions, biostatistics, pharmaceutical industry,
Survival analysis (Biometry) $x Mathematical models.
Mathematical statistics.
Statistics $x methods.
Nonparametric statistics.

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