Accounting for uncertainty in systematic bias in exposure estimates used in relative risk regression
Accounting for uncertainty in systematic bias in exposure estimates used in relative risk regression
复制标题
考虑相对风险回归中使用的暴露估计中系统偏差的不确定性
DOI：
10.2172/195768
复制
发表时间：
1995
影响因子：
3.6
通讯作者：
E. Gilbert
中科院分区：
文献类型：
--
作者：
E. Gilbert
在许多涉及暴露-应答关系的流行病学研究中，已知存在导致暴露测量系统偏倚的误差来源，但偏倚的幅度和性质尚不确定。开发了两种方法，使这种不确定性反映在置信限和其他统计推断中，并适用于队列和病例对照研究。第一种方法是基于对似然比统计的数值近似，第二种方法是基于分数统计的计算机模拟。这些方法被应用到从一个队列研究的工人在汉福德网站（1944-86）暴露于职业性的外部辐射的数据;合并数据的工人暴露在汉福德，橡树岭国家实验室，岩石平地武器厂;和人工数据集创建不同的样本量和风险估计的大小的影响进行检查。对于工人的数据，抽样的不确定性占主导地位，并占系统偏差的不确定性并没有大大修改置信限。然而，随着样本量的增加，考虑这些不确定性变得更加重要，当有兴趣比较或合并不同研究的结果时，建议使用。
In many epidemiologic studies addressing exposure-response relationships, sources of error that lead to systematic bias in exposure measurements are known to be present, but there is uncertainty in the magnitude and nature of the bias. Two approaches that allow this uncertainty to be reflected in confidence limits and other statistical inferences were developed, and are applicable to both cohort and case-control studies. The first approach is based on a numerical approximation to the likelihood ratio statistic, and the second uses computer simulations based on the score statistic. These approaches were applied to data from a cohort study of workers at the Hanford site (1944-86) exposed occupationally to external radiation; to combined data on workers exposed at Hanford, Oak Ridge National Laboratory, and Rocky Flats Weapons plant; and to artificial data sets created to examine the effects of varying sample size and the magnitude of the risk estimate. For the worker data, sampling uncertainty dominated and accounting for uncertainty in systematic bias did not greatly modify confidence limits. However, with increased sample size, accounting for these uncertainties became more important, and is recommended when there is interest in comparing or combining results from different studies.