Negative Controls¶
Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010). Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies. Epidemiology, 21(3), 383-388.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Signal Extraction
- The prime's misspecification-penalty result is the central caution of the field: when the noise model is wrong — an unmeasured confounder, a mis-specified functional form — the extracted effect acquires a bias bounded by the signal-noise overlap on that dimension, which is why analysts inspect residual confounding via negative controls and sensitivity analysis.
This sourceEstablishes negative controls and sensitivity analysis as diagnostics for residual confounding when the confounder (noise) model is misspecified.
Supported in partVerified against the work's full text
Lipsitch et al. support negative controls as a diagnostic for residual confounding, but not a bound on bias from signal-noise overlap nor sensitivity analysis.
“Despite these limitations, negative controls have value in alerting the analyst to possible residual confounding.”
- The prime's misspecification-penalty result is the central caution of the field: when the noise model is wrong — an unmeasured confounder, a mis-specified functional form — the extracted effect acquires a bias bounded by the signal-noise overlap on that dimension, which is why analysts inspect residual confounding via negative controls and sensitivity analysis.
Mechanisms¶
- Negative-Control Outcome Probe
- Its strength is that a moving negative control is nearly unarguable evidence of shared-source contamination, and it catches leakage that stratification and modeling miss — because it does not depend on having named the source correctly, only on a credible null.
This sourceUses a credible negative-control null to detect residual or unsuspected systematic error that ordinary adjustment may leave behind.
- Its strength is that a moving negative control is nearly unarguable evidence of shared-source contamination, and it catches leakage that stratification and modeling miss — because it does not depend on having named the source correctly, only on a credible null.
- Negative-Control Signature Panel
- Borrowing the logic of the experimental negative control, it asks what the mark looks like when the source is definitely not responsible.
This sourceDefines negative controls as exposures or outcomes expected not to be causally related, so an observed association can reveal confounding or bias.
- Borrowing the logic of the experimental negative control, it asks what the mark looks like when the source is definitely not responsible.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 1 of 3 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:54b1f1bd215e · see in the full table