Teaching about measurement error

Tutorials and tools for learning about handling measurement error in the statistical analysis

Toolkit for measurement error correction

  • Toolkit for measurement error correction using repeated measurements

  • Classical, systematic, heteroscedastic and differential measurement error

  • Correction methods: regression calibration, moment reconstruction, multiple imputation

  • Error in continuous exposures and categorized continuous exposures

MEM-Explorer

  • User-frendly Shiny app for interactive exploration of the impacts of measurement error and misclassification

  • Linear regression models

  • Classical, linear and Berkson error in categorical and continuous covariates

  • Based on the STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology

Other tools

  • Shiny app: "Impact of measurement error in a predictor" developed by Christian Gray, London School of Hygiene and Tropical Medicine (Link)

  • Shiny app: "Classical measurement error in linear, logit and Poisson regression" developed by Stefanie Muff, Norwegian University of Science and Technology (Link)

  • Shiny app: "Berkson measurement error in linear, logit and Poisson regression" developed by Stefanie Muff, Norwegian University of Science and Technology (Link)