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

By Marc Lipsitch, E. T. Tchetgen Tchetgen, T. Cohen. Epidemiology, 2010

1,341 citations recorded by the source corpus.

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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/730b1c3d7476589e4028f8d6ef367dc858a57264

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Citations in the corpus

Confounding-Robust Policy Improvement (2018, background): “Additionally, when alternative outcome data is available, other approaches such as negative controls can be used to provide a lower bound for [16].”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “A second approach is to consider outcome controls (18) for which no causal effect is expected, for example, brain cancer or pneumonia in our hormone therapy example.”
A confounding bridge approach for double negative control inference on causal effects (2018, positive): “A special case is the nondifferential assumption of Lipsitch et al. (2010) and Tchetgen Tchetgen (2014), which further requires W ⊥⊥ Y | U and does not allow for extra confounders of W –”
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality (2023, background): “…characterized as relying on covariance-based methods, effect inference does rely on logical reasoning approaches for determining validity of causal assumptions (Sharma et al., 2021) and identifying approaches to validation and sensitivity analyses (e.g., negative controls (Lipsitch et al., 2010)).”
Triangulation in aetiological epidemiology (2016, background): “In a third approach–negative control studies–we would expect an unbiased negative control study to be null and the ‘real’ study not-null if there is a true causal effect and the assumptions of this approach are met (see Table 1).(39,40) In many negative control studies there will not be a simple dic”