Measurement error, biases, and the validation of complex models

Abstract

There are three major points to this article: 1. Measurement error causes biases in regression fits. The line one would obtain if one could accurately measure exposure to environmental lead media will differ in important ways when one measures exposure with error. 2. 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. 3. Measurement error can falsely invalidate a correct (complex mechanistic) model. If one builds a model such as the IEUBK 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. While mean blood lead levels from such a process may be accurately predicted, in most cases one would expect serious under- or over-estimates of the proportion of the population whose blood lead level exceeds certain standards.