Buonaccorsi John P, Dalen Ingvild, Laake Petter, Hjartåker Anette, Engeset Dagrun, Thoresen Magne
Department of Mathematics and Statistics, University of Massachusetts, U.S.A.
Stat Med. 2015 Apr 15;34(8):1389-403. doi: 10.1002/sim.6420. Epub 2015 Jan 27.
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.
当我们观察容易出错的替代指标而非真实值时，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，特别是在营养流行病学中。校正测量误差已变得很普遍，回归校准是在连续协变量中考虑测量误差的最常用方法。我们考虑在有验证数据的情况下使用它，这些验证数据用于根据观察到的协变量校准真实值。我们考虑到在验证数据中可能未观察到真实值本身，而是观察到了所谓的参考测量值的情况。回归校准方法依赖于某些假设。本文研究了当这些假设中的一些被违反时，回归校准估计量中可能存在的偏差。更具体地说，我们考虑到以下事实：（i）参考测量值不一定是真实值的“合金金标准”（即无偏）；（ii）在验证数据中，可能存在导致替代指标和参考测量值的相关随机个体效应；（iii）校准模型本身在验证研究中可能与在主要研究中不同；也就是说，它不可移植。我们在先前工作的基础上进行扩展，以提供一个一般性结果，该结果刻画了由于上述任何组合的违反情况导致的回归校准估计量中的潜在偏差。然后，我们用挪威妇女与癌症研究的数据说明了一些一般性结果。
