Stayner Leslie, Vrijheid Martine, Cardis Elisabeth, Stram Daniel O, Deltour Isabelle, Gilbert Stephen J, Howe Geoffrey
Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois, Chicago, Illinois 60612-4394, USA.
Radiat Res. 2007 Dec;168(6):757-63. doi: 10.1667/RR0677.1.
Errors in the estimation of exposures or doses are a major source of uncertainty in epidemiological studies of cancer among nuclear workers. This paper presents a Monte Carlo maximum likelihood method that can be used for estimating a confidence interval that reflects both statistical sampling error and uncertainty in the measurement of exposures. The method is illustrated by application to an analysis of all cancer (excluding leukemia) mortality in a study of nuclear workers at the Oak Ridge National Laboratory (ORNL). Monte Carlo methods were used to generate 10,000 data sets with a simulated corrected dose estimate for each member of the cohort based on the estimated distribution of errors in doses. A Cox proportional hazards model was applied to each of these simulated data sets. A partial likelihood, averaged over all of the simulations, was generated; the central risk estimate and confidence interval were estimated from this partial likelihood. The conventional unsimulated analysis of the ORNL study yielded an excess relative risk (ERR) of 5.38 per Sv (90% confidence interval 0.54-12.58). The Monte Carlo maximum likelihood method yielded a slightly lower ERR (4.82 per Sv) and wider confidence interval (0.41-13.31).
在核工业工作人员癌症的流行病学研究中，暴露或剂量估计的误差是不确定性的主要来源。本文提出了一种蒙特卡罗最大似然法，可用于估计反映统计抽样误差和暴露测量不确定性的置信区间。该方法通过应用于对橡树岭国家实验室（ORNL）核工业工作人员的一项研究中所有癌症（不包括白血病）死亡率的分析进行了说明。蒙特卡罗方法用于生成10,000个数据集，根据剂量误差的估计分布为队列中的每个成员生成模拟校正剂量估计值。将Cox比例风险模型应用于这些模拟数据集中的每一个。生成了所有模拟的平均部分似然；从该部分似然估计中心风险估计值和置信区间。ORNL研究的传统未模拟分析得出每西弗的超额相对风险（ERR）为5.38（90%置信区间0.54 - 12.58）。蒙特卡罗最大似然法得出的ERR略低（每西弗4.82），置信区间更宽（0.41 - 13.31）。
