Outliers in Survival Analysis

Durdu Karasoy and Nuray Tuncer

Alphanumeric Journal, 2015, vol. 3, issue 2, 139-152

Abstract: Survival analysis is a collection of statistical methods for analyzing data where the outcome variable is the time until the occurrence of an event of interest. Outliers in survival anaysis calculated differently from classical regression analysis. Outlier detection methods in survival analysis are commonly carried out based on residuals and residual analysis. In survival analysis, there are different types of residuals that are Cox-Snell, Martingale, Schoenfeld, Deviance, Log-odds and Normal deviance residuals. There are methods which are DFBETA, LMAX and Likelihood Displacement values for detecting influential observations. The residuals are analyzed during the study which is applied on a stomach cancer data set and the outliers are detected. After omitting these outliers, model is set up again and results were found better.

Keywords: Influential Observations; Outliers; Residuals; Survival Analysis; Survival Models (search for similar items in EconPapers)
JEL-codes: C10 C14 C19 C24 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:anm:alpnmr:v:3:y:2015:i:2:p:139-152

DOI: 10.17093/aj.2015.3.2.5000149382

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