Little Mark P, Hamada Nobuyuki, Zablotska Lydia B
Radiation Epidemiology Branch, National Cancer Institute, Bethesda, MD 20892-9778 USA.
Biology and Environmental Chemistry Division, Sustainable System Research Laboratory, Central Research Institute of Electric Power Industry (CRIEPI), 1646 Abiko, Chiba 270-1194, Japan.
Res Sq. 2023 Aug 18:rs.3.rs-3248694. doi: 10.21203/rs.3.rs-3248694/v1.
There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient (\alpha) are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient (\beta) are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for (\beta) that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient (\beta) are substantially upwardly biased.
对于白血病和甲状腺癌等高辐射致癌性癌症，在中等和高辐射剂量水平下存在风险的直接证据。然而，对于许多癌症部位，有必要通过从中等和高剂量水平暴露组进行外推来评估风险，而这存在很大的不确定性。解决这一不确定性领域的关键在于剂量反应关系的建模以及系统和随机剂量测定误差对各暴露组分析的重要性。众所周知，测量误差会极大地改变这种关系的形状，从而改变推导得出的人群风险估计值。人们特别关注许多数据集中常见的共享误差问题，这在职业环境中尤为重要。我们提出了一种回归校准方法的改进方案，该方案特别适用于存在大量共享误差且真实剂量反应可能存在曲率的研究。此方法可用于存在伯克森误差和经典误差混合的情况。在拟合真实剂量反应存在大幅向上曲率以及不同（有时很大）数量的经典误差和伯克森误差的合成数据集时，我们表明，对于线性系数(\alpha)，所有方法的覆盖概率都接近期望水平，无论假定的伯克森误差和经典误差的大小如何，无论是共享的还是非共享的。然而，对于未调整和回归校准方法，二次系数(\beta)的覆盖概率通常过低，特别是对于较大的伯克森误差幅度，无论其是共享的还是非共享的。相比之下，蒙特卡罗最大似然法得出的(\beta)覆盖概率普遍过高。当共享和非共享的伯克森误差都很大时，扩展回归校准方法得出的覆盖概率过低，不过其他情况下表现良好，且覆盖度通常优于其他三种方法。一个显著特点是，除扩展回归校准外，所有方法对二次系数(\beta)的估计都存在大幅向上偏差。
