Methods to address measurement error in both Outcome and Covariates

2026-06-27

AI Summary

This study addresses the pervasive issue of measurement error in both outcome variables and multiple covariates within routinely collected biomedical data, such as electronic health records, which, if uncorrected, can induce analytical bias and misinform clinical decisions. For the first time within a tutorial framework, it systematically reviews and empirically compares several methods capable of simultaneously correcting measurement error in both outcomes and multiple covariates—including regression calibration, SIMEX, instrumental variable approaches, and modeling strategies leveraging validation subsamples. Through a unified illustrative example and publicly available code, the work not only clarifies the relative performance of these methods in real-world data to guide researchers’ methodological choices but also establishes a reproducible end-to-end analytical pipeline and highlights promising directions for future research.

Abstract

Biomedical research is increasingly relying on readily available routine data, such as electronic health records. Routinely collected data, as well as datasets from large cohorts, are often prone to measurement error which, if not addressed in analyses, can bias study results and ultimately mislead clinical decision-making and potentially harm patients. For this setting, methods that address errors in the outcome and multiple covariates are needed. In this tutorial, we will review available methods to address for errors in both outcomes and covariates. We will illustrate methods with use of a running example in order to compare the methods directly. Both the data and analytic code are provided for the user so that they may easily reproduce results in each example. We conclude the tutorial with a discussion of the different approaches and highlight areas of future work needed for this setting.

Problem

• measurement error
• outcome
• covariates
• biomedical research
• routine data

Innovation

• measurement error
• outcome error
• covariate error
• reproducible research
• tutorial