STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1—Basic theory and simple methods of adjustment
Methodological guidance demonstrates statistical approaches for mitigating measurement error in observational epidemiology, highlighting the utility of regression calibration.
Key Points
To review the theoretical concepts and analytical impacts of measurement error and misclassification in observational epidemiology and outline practical statistical methods for bias correction.
Reviewed classical, linear, and Berkson measurement error models alongside differential and nondifferential misclassification frameworks.
Evaluated sample size considerations and study design principles for ancillary validation studies and primary epidemiological investigations.
Demonstrated regression calibration and simulation extrapolation (SIMEX) bias-adjustment methods using data from the Observing Protein and Energy (OPEN) dietary validation study.
Measurement error and misclassification distort exposure-outcome effect estimates, reduce statistical power, and bias regression coefficients in observational studies.
Ancillary validation studies provide the essential statistical parameters needed to quantify measurement error variance and structure.
Simpler statistical correction methods, specifically regression calibration and SIMEX, successfully mitigate bias in continuous covariate regression models when supported by available software packages.