Brief Report: Negative Controls to Detect Selection Bias and Measurement Bias in Epidemiologic Studies - PubMed (original) (raw)

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

Benjamin F Arnold et al. Epidemiology. 2016 Sep.

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.

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Conflict of interest statement

This study was funded in part by National Institutes of Health Grants 1R01HD078912, 1R21HD076216, 1R03HD076066, and 1K01AI119180.

The authors report no conflicts of interest.

Figures

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*.

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Brief Report: Negative Controls to Detect Selection Bias and Measurement Bias in Epidemiologic Studies - PubMed (original) (raw)