Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, Bremen, 28359, Germany.
Epidemiological Cancer Registry of Lower Saxony, Industriestraße 9, Oldenburg, 26121, Germany.
BMC Cancer. 2018 Apr 4;18(1):384. doi: 10.1186/s12885-018-4259-z.
Common cancer monitoring practice is seldom prospective and rather driven by public requests. This study aims to assess the performance of a recently developed prospective cancer monitoring method and the statistical tools used, in particular the sequential probability ratio test in regard to specificity, sensitivity, observation time and heterogeneity of size of the geographical unit.
A simulation study based on a predefined selection of cancer types, geographical unit and time period was set up. Based on the population structure of Lower Saxony the mean number of cases of three diagnoses were randomly assigned to the geographical units during 2008-2012. A two-stage monitoring procedure was then executed considering the standardized incidence ratio and sequential probability ratio test. Scenarios were constructed differing by the simulation of clusters, significance level and test parameter indicating a risk to be elevated.
Performance strongly depended on the choice of the test parameter. If the expected numbers of cases were low, the significance level was not fully exhausted. Hence, the number of false positives was lower than the chosen significance level suggested, leading to a high specificity. Sensitivity increased with the expected number of cases and the amount of risk and decreased with the size of the geographical unit.
The procedure showed some desirable properties and is ready to use for a few settings but demands adjustments for others. Future work might consider refinements of the geographical structure. Inhomogeneous unit size could be addressed by a flexible choice of the test parameter related to the observation time.
常见的癌症监测实践很少是前瞻性的，而是由公众的要求驱动的。本研究旨在评估一种新开发的前瞻性癌症监测方法及其使用的统计工具（特别是序贯概率比检验）在特异性、敏感性、观察时间和地理单位大小的异质性方面的性能。
基于预先设定的癌症类型、地理单位和时间段，设计了一项模拟研究。根据下萨克森州的人口结构，在 2008 年至 2012 年期间，将三种诊断的平均病例数随机分配到地理单位。然后，采用考虑标准化发病比和序贯概率比检验的两阶段监测程序。通过模拟集群、显著性水平和表示风险升高的测试参数，构建了不同的情景。
性能强烈取决于测试参数的选择。如果预期病例数较低，则未充分耗尽显著性水平。因此，假阳性的数量低于所选的显著性水平建议值，导致高特异性。敏感性随预期病例数、风险量和地理单位大小的增加而增加，随地理单位大小的增加而降低。
该程序表现出一些理想的特性，并且已经准备好用于少数环境，但需要针对其他环境进行调整。未来的工作可能会考虑改进地理结构。通过与观察时间相关的测试参数的灵活选择，可以解决不均匀的单位大小问题。
