A general, simple, robust method to account for measurement error when analyzing data with an internal validation subsample

TL;DR

This paper introduces a simple, robust method for accounting for measurement error in epidemiologic data using an internal validation subsample, improving bias and variance in statistical analysis.

Contribution

It presents a general, error-robust analytic approach that does not depend on the specific error relationship, applicable to various measurement error types.

Findings

Reduces bias compared to models ignoring measurement error.

Decreases variance relative to models using only validation data.

Robust across different types of measurement error.

Abstract

Background: Measurement errors in terms of quantification or classification frequently occur in epidemiologic data and can strongly impact inference. Measurement errors may occur when ascertaining, recording or extracting data. Although the effects of measurement errors can be severe and are well described, simple straight forward general analytic solutions are not readily available for statistical analysis and measurement error is frequently not acknowledged or accounted for. Generally, to account for measurement error requires some data where we can observe the variables once with and once without error, to establish the relationship between the two. Methods: Here we describe a general method accounting for measurement error in outcome and/or predictor variables for the parametric regression setting when there is a validation subsample where variables are measured once with and once without error. The method does not describe and thus does not depend on the particular relation between the variables measured with and without error, and is generally robust to the type of measurement error, for example nondifferential, differential or Berkson errors. Results: Simulation studies show how the method reduces bias compared to models based upon variables measured with error alone and reduces variances compared to models based upon the variables measured without error in the validation subsample alone. Conclusion: The proposed estimator has favorable properties in terms of bias and variance, is easily derived empirically, and is robust to different types of measurement error. This method should be a valuable tool in the analysis of data with measurement error.