The effect that measurement error of predictor variables has on regression inference is well known in the statistical literature. However, the influence of measurement error on the ability to quantify relationships between chemical stressors and biological responses has received little attention in ecotoxicology. We present a common data-collection scenario and demonstrate that the relationship between explanatory and response variables is consistently underestimated when measurement error is ignored. A straightforward extension of the regression calibration method is to use a nonparametric method to smooth the predictor variable with respect to another covariate (e.g., time) and using the smoothed predictor to estimate the response variable. We conducted a simulation study to compare the effectiveness of the proposed method to the naive analysis that ignores measurement error. We conclude that the method satisfactorily addresses the problem when measurement error is moderate to large, and does not result in a noticeable loss of power in the case where measurement error is absent.
- Arkansas River, CO
- Measurement error
- Nonparametric function smoothing
- Regression calibration
ASJC Scopus subject areas
- Management, Monitoring, Policy and Law
- Health, Toxicology and Mutagenesis