Egleston Brian L, Wong Yu-Ning
Biostatistics Facility, Fox Chase Cancer Center, Philadelphia, PA, USA.
Stat Med. 2009 May 1;28(10):1498-511. doi: 10.1002/sim.3557.
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.
存在大量缺失数据是行政数据和癌症登记数据中的常见问题。我们提出一种敏感性分析方法，以评估在使用威布尔比例风险回归进行的生存分析中，一个可能非随机缺失的协变量的影响。我们将该方法应用于一项关于转移性肾癌患者手术分级缺失对术后死亡率结果影响的调查。数据来自监测、流行病学和最终结果（SEER）登记处，该登记处提供了接受减瘤性肾切除术患者的基于人群的信息。肿瘤分级是局限性和转移性肾癌患者风险分层的重要组成部分。SEER中许多转移性肾癌患者缺失肿瘤分级信息。我们发现手术具有保护作用，但效果的大小取决于关于分级与缺失之间关系的假设。
