Using measurement error parameters from validation data.

Measurement error is ubiquitous in the data used for epidemiologic research and can lead to meaningful information bias. Analytic approaches to address measurement error and quantitative bias analyses examining the potential impact of measurement error on study results often leverage validation data that provides information about the relationship between the true measure and the available imperfect measure, quantified by measurement error parameters such as sensitivity and specificity in the binary case. Leveraging validation data often requires transporting these measurement error parameters from the validation data to the target sample of interest (that may or may not include individuals from the validation data). In this paper we examine the independence assumptions required to transport measurement error parameters from the validation data to the target sample, highlighting how the required assumption differs depending on the form of the measurement error parameters (i.e., whether it is the true measure conditional on the imperfect measure or vice versa). We then illustrate how diagrams can clarify the conditions under which the required assumptions hold and thus what measurement error parameters can be validly transported. This work provides practical tools for epidemiologists to address measurement error using validation data in applied research.