Carroll R J, Galindo C D
Department of Statistics, Texas A&M University, College Station 77843-3143, USA.
Environ Health Perspect. 1998 Dec;106 Suppl 6(Suppl 6):1535-9. doi: 10.1289/ehp.98106s61535.
Measurement error causes biases in regression fits. If one could accurately measure exposure to environmental lead media, the line obtained would differ in important ways from the line obtained when one measures exposure with error. The effects of measurement error vary from study to study. It is dangerous to take measurement error corrections derived from one study and apply them to data from entirely different studies or populations. Measurement error can falsely invalidate a correct (complex mechanistic) model. If one builds a model such as the integrated exposure uptake biokinetic model carefully, using essentially error-free lead exposure data, and applies this model in a different data set with error-prone exposures, the complex mechanistic model will almost certainly do a poor job of prediction, especially of extremes. Although mean blood lead levels from such a process may be accurately predicted, in most cases one would expect serious underestimates or overestimates of the proportion of the population whose blood lead level exceeds certain standards.
测量误差会导致回归拟合出现偏差。如果能够准确测量环境铅介质的暴露情况，得到的回归线将在重要方面不同于存在测量误差时所得到的回归线。测量误差的影响因研究而异。将源自一项研究的测量误差校正应用于完全不同的研究或人群的数据是危险的。测量误差可能会错误地使正确的（复杂机制）模型无效。如果精心构建一个模型，如综合暴露吸收生物动力学模型，使用基本无误差的铅暴露数据，并将该模型应用于具有易出错暴露情况的不同数据集中，复杂机制模型几乎肯定会在预测方面表现不佳，尤其是对极端情况的预测。虽然通过这样的过程可能会准确预测平均血铅水平，但在大多数情况下，人们会预期对血铅水平超过某些标准的人群比例会严重低估或高估。
