
Accession Number : ADA119473
Title : Outliers Matter in Survival Analysis,
Corporate Author : RAND CORP SANTA MONICA CA
Personal Author(s) : Hall,Gaineford J. ; Rogers,William H. ; Pregibon,Daryl
Report Date : APR 1982
Pagination or Media Count : 41
Abstract : Generally, but not always, the most influential observations (cases) possess the largest CoxSnell residuals. A case which has a variable far out in the factor space may be more influential than a case with a large residual. Either kind of outlier can affect inference. The plots of the log survival curve of the residuals and the corresponding variancestabilized transformation generally do not indicate the importance (influence) of large residuals on estimated parameters. Regression parameter estimates from the Cox proportional hazards model are just as sensitive to influential cases as are fully parametric models. Influential observations often suggest other modelling deficiencies, such as a poorlyspecified factor, nonproportional hazards, or an omitted variable. The analyses indicate how broadlybased the conclusions are. We found no evidence to support automatic exclusion of outlying observations. Outlier diagnostics based on linearization (the loglikelihood and its derivatives) work very well. The measure of importance tends to be compressed at its upper end, so the impact of very influential observations tends to be understated.
Descriptors : *Statistical analysis, *Linear regression analysis, *Diagnosis(Medicine), Survival(Personnel), Maximum likelihood estimation, Asymptotic normality, Linearity, Least squares method, Observation, Points(Mathematics), Residuals, Weibull density functions, Cancer, Case studies, Medical research, Data bases
Subject Categories : Medicine and Medical Research
Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE