Brief Report: Negative Controls to Detect Selection Bias and Measurement Bias in Epidemiologic Studies

Abstract

Biomedical laboratory experiments routinely use negative controls to identify possible sources of bias, but epidemiologic studies have infrequently used this type of control in their design or measurement approach. Recently, epidemiologists proposed the routine use of negative controls in observational studies and defined the structure of negative controls to detect bias due to unmeasured confounding. We extend this previous study and define the structure of negative controls to detect selection bias and measurement bias in both observational studies and randomized trials. We illustrate the strengths and limitations of negative controls in this context using examples from the epidemiologic literature. Given their demonstrated utility and broad generalizability, the routine use of prespecified negative controls will strengthen the evidence from epidemiologic studies.

FIGURE 1.

Simplified causal diagrams of selection bias for exposure A and outcome Y along with negative control exposures (N_A) and outcomes (N_Y). In all four structures, selection bias results from conditioning on C, a common descendant of (A) exposure A and outcome Y, (B) cause of exposure U_A and outcome Y, (C) exposure A and cause of outcome U_Y, or (D) cause of exposure U_A and cause of outcome U_Y.

FIGURE 2.

Simplified causal diagrams of differential measurement error for an exposure A that causes outcome Y. The basic structures for outcome measurement error (A) and exposure measurement error (B) are summarized along with negative control exposures (N_A) and outcomes (N_Y). U_Y represents other causes of the measured value of Y* and U_A represents other causes of the measured value of A*.