Sensitivity analysis to investigate the impact of a missing covariate on survival analyses using cancer registry data.
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.
Egleston
Brian L
BL
Biostatistics Facility, Fox Chase Cancer Center, Philadelphia, PA, USA. brian.egleston@fccc.edu
Wong
Yu-Ning
YN
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000401:mortality / Q000473:pathology / Q000556:secondary / Q000601:surgery; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000401:mortality* / Q000628:therapy*; D016016:Proportional Hazards Models; D012042:Registries; D018426:SEER Program / Q000706:statistics & numerical data; D012680:Sensitivity and Specificity; D016019:Survival Analysis