Li Yi, Tiwari Ram C
Harvard School of Public Health and Dana-Farber Cancer Institute.
Biometrics. 2008 Dec;64(4):1280-6. doi: 10.1111/j.1541-0420.2008.01002.x. Epub 2008 Mar 27.
Monitoring and comparing trends in cancer rates across geographic regions or over different time periods have been major tasks of the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) Program as it profiles healthcare quality as well as decides healthcare resource allocations within a spatial-temporal framework. A fundamental difficulty, however, arises when such comparisons have to be made for regions or time intervals that overlap, for example, comparing the change in trends of mortality rates in a local area (e.g., the mortality rate of breast cancer in California) with a more global level (i.e., the national mortality rate of breast cancer). In view of sparsity of available methodologies, this article develops a simple corrected Z-test that accounts for such overlapping. The performance of the proposed test over the two-sample "pooled"t-test that assumes independence across comparison groups is assessed via the Pitman asymptotic relative efficiency as well as Monte Carlo simulations and applications to the SEER cancer data. The proposed test will be important for the SEER * STAT software, maintained by the NCI, for the analysis of the SEER data.
监测和比较不同地理区域或不同时间段的癌症发病率趋势，一直是美国国立癌症研究所（NCI）的监测、流行病学和最终结果（SEER）计划的主要任务，该计划旨在描绘医疗质量，并在时空框架内决定医疗资源的分配。然而，当必须对重叠的区域或时间间隔进行此类比较时，就会出现一个基本难题，例如，将局部地区（如加利福尼亚州乳腺癌死亡率）的死亡率趋势变化与更宏观层面（即全国乳腺癌死亡率）进行比较。鉴于可用方法的稀缺性，本文开发了一种简单的校正Z检验，以考虑这种重叠情况。通过皮特曼渐近相对效率、蒙特卡罗模拟以及对SEER癌症数据的应用，评估了所提出的检验相对于假设比较组独立的两样本“合并”t检验的性能。所提出的检验对于由NCI维护的SEER * STAT软件分析SEER数据将具有重要意义。
