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Sensitivity analysis to investigate the impact of a missing covariate on survival analyses using cancer registry data
Author(s) -
Egleston Brian L.,
Wong YuNing
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3557
Subject(s) - covariate , missing data , cancer registry , medicine , surveillance, epidemiology, and end results , proportional hazards model , kidney cancer , epidemiology , cancer , survival analysis , statistics , population , oncology , environmental health , mathematics
Abstract Having substantial missing data is a common problem in administrative and cancer registry data. We propose a sensitivity analysis to evaluate the impact of a covariate that is potentially missing not at random in survival analyses using Weibull proportional hazards regressions. We apply the method to an investigation of the impact of missing grade on post‐surgical mortality outcomes in individuals with metastatic kidney cancer. Data came from the Surveillance Epidemiology and End Results (SEER) registry which provides population‐based information on those undergoing cytoreductive nephrectomy. Tumor grade is an important component of risk stratification for patients with both localized and metastatic kidney cancer. Many individuals in SEER with metastatic kidney cancer are missing tumor grade information. We found that surgery was protective, but that the magnitude of the effect depended on assumptions about the relationship of grade with missingness. Copyright © 2009 John Wiley & Sons, Ltd.