A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods
A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods
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DOI：
10.1038/s41598-024-56967-6
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发表时间：
2024-03-19
影响因子：
4.6
通讯作者：
Zablotska，Lydia B.
中科院分区：
文献类型：
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作者：
Little，Mark P.;Hamada，Nobuyuki;Zablotska，Lydia B.
对于许多癌症部位，低剂量风险尚不清楚，必须从暴露于更高剂量水平的群体中观察到的风险推断出来。测量误差会极大地改变剂量反应形状，从而改变推断的风险。即使在直接测量低剂量暴露的研究中，与剂量估计值的大小相关的测量误差也可能很大，从而扭曲群体风险估计。最近，处理共享错误的方法受到了相当多的关注，这些错误在许多数据集中很常见，在职业和环境环境中尤其重要。在本文中，我们测试了贝叶斯模型平均（BMA）和频率模型平均（FMA）方法，其中第一个类似于所谓的贝叶斯二维蒙特卡罗（2DMC）方法，并且都是最近提出的，针对回归校准方法的最新提出的修改，即扩展回归校准（ERC）方法，该方法特别适合于存在大量共享误差并且真实剂量响应中也可能存在曲率的研究。当假设线性模型时，带有 BMA 方法的准 2DMC 表现良好，但当假设线性二次模型时表现非常差，当共享 Berkson 误差幅度较大 (50%) 时，线性和二次剂量系数的覆盖概率均低于 5%。对于线性模型，偏差通常低于 10%。然而，使用线性二次模型，它会对线性系数和二次系数产生显着偏差（10 倍）的估计，其中线性系数被高估，二次系数被低估。当假设线性模型时，FMA 的性能与带有 BMA 的准 2DMC 一样好，并且通常在线性二次模型中表现得更好，尽管二次系数的覆盖概率都太高。然而，线性和二次系数都有明显的向上偏差，特别是当伯克森误差很大时。相比之下，当共享和非共享 Berkson 误差都很大 (50%) 时，ERC 产生的覆盖概率太低，尽管在其他方面它表现良好，并且覆盖范围通常优于采用 BMA 或 FMA 方法的准 2DMC，特别是对于线性二次模型。各种剂量下预测相对风险的偏差通常对于 ERC 来说是最小的，而对于使用 BMA 和 FMA 方法的准 2DMC 来说最大（除了未调整的回归），标准回归校准和蒙特卡罗最大似然法在预测相对风险中表现出的偏差通常介于 ERC 和其他两种方法之间。一般来说，ERC 在所呈现的情况下表现最佳，并且在剂量反应中可能存在重大共同误差或可疑曲率的情况下应该选择该方法。
For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose–response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) 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. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.