Sposto R, Preston D L, Shimizu Y, Mabuchi K
Department of Statistics, Radiation Effects Research Foundation, Hiroshima City, Japan.
Biometrics. 1992 Jun;48(2):605-17.
We used the EM algorithm in the context of a joint Poisson regression analysis of cancer and non-cancer mortality in the Radiation Effects Research Foundation (RERF) Life Span Study (LSS) to assess whether the observed increased risk of non-cancer death due to radiation exposure (Shimizu et al., RERF Technical Report 02-91, 1991) can be attributed solely to misclassification of cancer as non-cancer on death certificates. We show that greater levels of dose-independent misclassification than are indicated by a series of autopsies conducted on a subset of LSS members would be required to explain the non-cancer dose response, but that a relatively small amount of dose-dependence in the misclassification of cancer would explain the result. The adjustment for misclassification also results in higher risk estimates for cancer mortality. We review applications of similar statistical methods in other contexts and discuss extensions of the methods to more than two causes of death.
我们在辐射效应研究基金会（RERF）寿命研究（LSS）中，对癌症和非癌症死亡率进行联合泊松回归分析的背景下，使用期望最大化（EM）算法，以评估观察到的因辐射暴露导致的非癌症死亡风险增加（清水等人，RERF技术报告02 - 91，1991）是否仅可归因于死亡证明上癌症被误分类为非癌症。我们表明，要解释非癌症剂量反应，所需的与剂量无关的误分类水平要高于对LSS成员子集进行的一系列尸检所显示的水平，但癌症误分类中相对较小的剂量依赖性就能解释该结果。对误分类的调整也会导致癌症死亡率的风险估计值更高。我们回顾了类似统计方法在其他背景下的应用，并讨论了将这些方法扩展到两种以上死亡原因的情况。
