关于癌症登记数据分析中的一个基本问题
On a fundamental problem in the analysis of cancer registry data
摘要：
在癌症登记数据的流行病学研究中，主要关注的往往是对癌症死亡的推断，而不是总生存率。由于癌症登记中的死亡原因不容易收集或不一定可靠，因此引入了一些特殊的方法，并通过使用相对生存率和净生存率的概念而广泛使用。在对这些指标进行推断时，利用一般人群的外部生命表来调整非癌症死亡对总生存率的影响。这种调整的有效性依赖于这样的假设，即外部生命表中的死亡率近似于癌症患者的非癌症死亡率。然而，用于计算生命表的人口可能包括癌症死亡和癌症患者。Talbäck和Dickman提出的敏感性分析需要额外的信息，而这些信息往往不容易获得。我们提出了一种方法来推断净生存占潜在存在的癌症患者和癌症死亡的生命表中的一般人群。调整的想法是考虑生命表中癌症死亡率与癌症登记中癌症死亡率的对应性。我们实现了一种新的方法来调整癌症登记处的癌症死亡率，而无需对癌症登记处的标准分析提供任何额外信息。我们的模拟研究表明，所提出的方法成功地消除了偏见。我们说明了所提出的方法与癌症登记数据在英国。
英文摘要：
In epidemiology research with cancer registry data, it is often of primary interest to make inference on cancer death, not overall survival. Since cause of death is not easy to collect or is not necessarily reliable in cancer registries, some special methodologies have been introduced and widely used by using the concepts of the relative survival ratio and the net survival. In making inference of those measures, external life tables of the general population are utilized to adjust the impact of non-cancer death on overall survival. The validity of this adjustment relies on the assumption that mortality in the external life table approximates non-cancer mortality of cancer patients. However, the population used to calculate a life table may include cancer death and cancer patients. Sensitivity analysis proposed by Talbäck and Dickman to address it requires additional information which is often not easily available. We propose a method to make inference on the net survival accounting for potential presence of cancer patients and cancer death in the life table for the general population. The idea of adjustment is to consider correspondence of cancer mortality in the life table and that in the cancer registry. We realize a novel method to adjust cancer mortality in the cancer registry without any additional information to the standard analyses of cancer registries. Our simulation study revealed that the proposed method successfully removed the bias. We illustrate the proposed method with the cancer registry data in England.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
