Measurement error and misclassification

Aim

Increase the awareness of the problems caused by measurement error and misclassification in statistical analyses and remove barriers to use statistical methods that deal with such problems by publishing scientific articles and presenting papers and workshops at conferences. See also Freedman and Kipnis (2018).

Key messages

Only a minority of published papers present estimates that are adjusted for measurement error.
Considering measurement error is necessary because it may have an impact on the study results.
Special statistical methods are used to account for measurement error
Additional information is required about the type and size of the measurement error to adjust for measurement error.

Recent publications

Videos: STRATOS TIPS on Measurement Error

Part 1: What is measurement error?

Part 2: Why does measurement error matter?

Part 3: What can we do about measurement error?

Scientific paper on implementing regression calibration

Boe LA, Shaw PA, Midthune D, Gustafson P, Kipnis V, Park E, Sotres-Alvarez D, Freedman L, on behalf of the Measurement Error and Misclassification Topic Group (TG4) of the STRATOS Initiative (2024). Issues in implementing regression calibration analyses. American Journal of Epidemiology. Link

STRATOS Initiative

The topic group "Measurement error and misclassification" is a member of the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies) which is a large collaboration of experts in many different areas of biostatistical research. Ongoing research, discussions and activities within STRATOS are conducted in nine topic groups and several cross-cutting panels.