Does adjustment for measurement error induce positive bias if there is no true association?

TL;DR

This paper investigates whether adjusting for measurement error can cause false positive associations in epidemiological studies and finds that it generally does not, unless priors are improperly manipulated, emphasizing cautious interpretation.

Contribution

It provides simulation evidence that measurement error adjustment does not induce positive bias when no true association exists, clarifying misconceptions in epidemiological practice.

Findings

Adjustment does not induce positive bias if priors are properly set.

Misuse of priors can lead to false positive associations.

Focus should be on variability bounds, not just point estimates.

Abstract

This article is a response to an off-the-record discussion that I had at an international meeting of epidemiologists. It centered on a concern, perhaps widely spread, that measurement error adjustment methods can induce positive bias in results of epidemiological studies when there is no true association. I trace the possible history of this supposition and test it in a simulation study of both continuous and binary health outcomes under a classical multiplicative measurement error model. A Bayesian measurement adjustment method is used. The main conclusion is that adjustment for the presumed measurement error does not 'induce' positive associations, especially if the focus of the interpretation of the result is taken away from the point estimate. This is in line with properties of earlier measurement error adjustment methods introduced to epidemiologists in the 1990s. An heuristic argument is provided to support the generalizability of this observation in the Bayesian framework. I find that when there is no true association, positive bias can only be induced by indefensible manipulation of the priors, such that they dominate the data. The misconception about bias induced by measurement error adjustment should be more clearly explained during the training of epidemiologists to ensure the appropriate (and wider) use of measurement error correction procedures. The simple message that can be derived from this paper is: 'Do not focus on point estimates, but mind the gap between boundaries that reflect variability in the estimate'. And of course: 'Treat measurement error as a tractable problem that deserves much more attention than just a qualitative (throw-away) discussion'.