Negative Controls

A Tool for Detecting Confounding and Bias in Observational Studies

Bibliographic Data

ID23317204
AuthorsMarc Lipsitch (0000-0003-1504-9213, Center for Disease Dynamics, Economics & Policy, corresponding author), Eric Tchetgen Tchetgen (Center for Disease Dynamics, Economics & Policy), Ted Cohen (0000-0002-8091-7198, Brigham and Women's Hospital)
Year2010
Volume21
Issue3
Pages383-388
Publication date2010-05-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueEpidemiology (JOURNAL)
Journal identifiersISSN: 1044-3983 • E-ISSN: 1531-5487
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/ede.0b013e3181d61eeb
PMID20335814
PMCIDPMC3053408
OpenAlexW2009428399
LanguageEN
Citations received61
References cited15

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.

Biology · Causal inference · Clinical study design · Clinical trial · Confounding · Econometrics · Inference · Mendelian randomization · Observational study · Pathology · Population · Spurious relationship · Statistics · Type I and type II errors · Advanced Causal Inference Techniques · Artificial Intelligence · Computer Science · Mathematics · Medicine · Statistical Methods and Bayesian Inference

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Open DOISci-HubOpen Access