Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies

Lipsitch, Marca,b,c; Tchetgen Tchetgen, Erica,c,d; Cohen, Teda,c,e


Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010;21:383–388.

The sentence beginning at the bottom of page 386 should read: “If A and B are perfectly U-comparable and B does not cause Y, then a null finding of B-Y means that the A-Y association is unbiased.”

In the published version, the phrase “null finding of B-Y” was written incorrectly as “null finding of A-N.”

Epidemiology. 21(4):589, July 2010.

doi: 10.1097/EDE.0b013e3181d61eeb
Methods: Original Article

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.

From the aDepartment of Epidemiology, bDepartment of Immunology and Infectious Diseases, cCenter for Communicable Disease Dynamics, and dDepartment of Biostatistics, Harvard School of Public Health, Boston, MA; and eDivision of Global Health Equity, Brigham and Women's Hospital, Boston, MA.

Submitted 19 March 2009; accepted 12 October 2009.

Supported by NIH 5U01GM076497 and 1U54GM088558 (Models of Infectious Disease Agent Study) to ML.

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Correspondence: Marc Lipsitch, Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115. E-mail:

© 2010 Lippincott Williams & Wilkins, Inc.