Negative Controls

TLDR

Noncausal associations threaten causal inference in observational studies, prompting development of design and analysis techniques, while experimental studies rely on standardization and randomization to mitigate such errors; epidemiology uses negative controls to detect confounding and other biases. The study argues that using negative controls in laboratory experiments can detect both suspected and unsuspected sources of spurious causal inference. The authors distinguish exposure and outcome negative controls, provide epidemiologic examples, and outline conditions under which they detect confounding. They conclude that negative controls should be more widely used in observational studies and that further work is needed to define conditions for their sensitivity to other errors.

Abstract

Noncausal associations between exposures and outcomes are a threat to validity of causal inference in observational studies. Many techniques have been developed for study design and analysis to identify and eliminate such errors. Such problems are not expected to compromise experimental studies, where careful standardization of conditions (for laboratory work) and randomization (for population studies) should, if applied properly, eliminate most such noncausal associations. We argue, however, that a routine precaution taken in the design of biologic laboratory experiments--the use of "negative controls"--is designed to detect both suspected and unsuspected sources of spurious causal inference. In epidemiology, analogous negative controls help to identify and resolve confounding as well as other sources of error, including recall bias or analytic flaws. We distinguish 2 types of negative controls (exposure controls and outcome controls), describe examples of each type from the epidemiologic literature, and identify the conditions for the use of such negative controls to detect confounding. We conclude that negative controls should be more commonly employed in observational studies, and that additional work is needed to specify the conditions under which negative controls will be sensitive detectors of other sources of error in observational studies.