Sensitivity of regression calibration to non-perfect validation data with application to the Norwegian Women and Cancer Study
Sensitivity of regression calibration to non-perfect validation data with application to the Norwegian Women and Cancer Study
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DOI：
10.1002/sim.6420
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发表时间：
2015-04-15
影响因子：
2
通讯作者：
Thoresen, Magne
中科院分区：
文献类型：
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作者：
Buonaccorsi, John P.;Dalen, Ingvild;Thoresen, Magne
当我们观察容易出错的替代物，而不是真实值时，就会出现测量误差。这在观察性研究中很常见，在流行病学中尤其如此，在营养流行病学中尤其如此。对测量误差的校正已经变得很普遍，回归校正是解决连续协变量中测量误差的最流行的方法。我们考虑在有验证数据的情况下使用它，验证数据用于校准给定观察到的协变量的真值。我们允许在验证数据中可能没有观察到真值本身的情况，而是观察到所谓的参考测量。回归校正方法依赖于一定的假设，当某些假设被违反时，本文检验回归校正估计量中可能存在的偏差。更具体地说，我们考虑到这样一个事实：(I)参考测量不一定是真实价值的合金金本位“(即，无偏)；(Ii)验证数据中可能存在导致替代测量和参考测量的相关随机主题效应；以及(Iii)验证研究中的校准模型本身可能与主要研究中的不同；即，它不可运输。我们对以前的工作进行了扩展，以提供一个一般性的结果，该结果表征了由于上述违规行为的任何组合而导致的回归校准估计量中的潜在偏差。然后，我们用挪威妇女和癌症研究的数据说明了一些一般性的结果。版权所有(C)2015 John Wiley&Sons，Ltd.
Measurement error occurs when we observe error-prone surrogates, rather than true values. It is common in observational studies and especially so in epidemiology, in nutritional epidemiology in particular. Correcting for measurement error has become common, and regression calibration is the most popular way to account for measurement error in continuous covariates. We consider its use in the context where there are validation data, which are used to calibrate the true values given the observed covariates. We allow for the case that the true value itself may not be observed in the validation data, but instead, a so-called reference measure is observed. The regression calibration method relies on certain assumptions.This paper examines possible biases in regression calibration estimators when some of these assumptions are violated. More specifically, we allow for the fact that (i) the reference measure may not necessarily be an alloyed gold standard' (i.e., unbiased) for the true value; (ii) there may be correlated random subject effects contributing to the surrogate and reference measures in the validation data; and (iii) the calibration model itself may not be the same in the validation study as in the main study; that is, it is not transportable. We expand on previous work to provide a general result, which characterizes potential bias in the regression calibration estimators as a result of any combination of the violations aforementioned. We then illustrate some of the general results with data from the Norwegian Women and Cancer Study. Copyright (c) 2015 John Wiley & Sons, Ltd.