Recent Contributions of STRATOS Topic Group 4: Measurement Error and Misclassification

STRATOS Topic Group 4 focuses on providing guidance for appropriate statistical methods for data with measurement error or misclassification, where observed measurements differ from those we wish to observe. In this talk, we will provide an overview of recent contributions from the group. Two projects have focused on the categorization of continuous variables that are measured with error. This can result in misclassification - where measurements are categorized into the wrong category - with varied and complex implications for analysis. In one project, we explore various misconceptions about categorized error-prone variables. These include that categorization can assist with finding the shape of the exposure-outcome relationship, and that categorization can mitigate bias due to measurement error. We have also explored how to account for measurement error in such analyses via a relatively easy-to-implement method that combines MacMahon's method and regression calibration. We will also summarize separate work on the impact of measurement error on prediction. First, we discuss how predicted continuous variables have Berkson error, describe the impact of this error on inference, and propose a simple method to correct for bias in this context. We then also consider post-prediction inference in the context of regression calibration, including the need to account for the uncertainty in the regression calibration estimates of error-prone variables. For this, we discuss a stacked estimating equation approach, and an associated R package. Lastly, we briefly mention new areas of focus and will discuss outreach work in the form of short videos introducing the key themes, and challenges, of measurement error to a general audience.

measurement error

misclassification

regression calibration

Berkson error

categorization