Negative controls
Negative controls are variables that meant to help when the study design is suspected to be invalid because of unmeasured confounders that are correlated with both the treatment and the outcome.
[1] Confounding is a critical issue in observational studies because it can lead to biased or misleading conclusions about relationships between variables.
If confounding is not properly accounted for, researchers might incorrectly attribute an effect to the exposure when it is actually due to another factor.
This can result in incorrect policy recommendations, ineffective interventions, or flawed scientific understanding.
For example, in a study examining the relationship between physical activity and heart disease, failure to control for diet, a potential confounder, could lead to an overestimation or underestimation of the true effect of exercise.
[2] Falsification tests are a robustness-checking technique used in observational studies to assess whether observed associations are likely due to confounding, bias, or model misspecification rather than a true causal effect.
These tests help validate findings by applying the same analytical approach to a scenario where no effect is expected.
If an association still appears where none should exist, it raises concerns that the primary analysis may suffer from confounding or other biases.
A Negative control test can reject study design, but it cannot validate them.
Negative controls are increasingly used in the epidemiology literature,[3] but they show promise in social sciences fields[4] such as economics.
Lousdal et al.[6] examined the effect of screening participation on death from breast cancer.
They hypothesized that screening participants are healthier than non-participants and, therefore, already at baseline have a lower risk of breast-cancer death.
Dental care participation was taken to be NCE, as it is assumed to be a good proxy of health attentive behavior.
NCE is a variable that should not causally affect the outcome, but may suffer from the same confounding as the exposure-outcome relationship in question.
If an association is found, then it through the unmeasured confounder, and since the NCE and treatment share the same confounding mechanism, there is an alternative path, apart from the direct path from the treatment to the outcome.
Nonetheless, Yerushalmy found a statistical association, And as a result, it casts doubt on the proposition that cigarette smoking causally interferes with intrauterine development of the fetus.
NCO is a variable that is not causally affected by the treatment, but suspected to have a similar confounding mechanism as the treatment-outcome relationship.
If the study design is valid, there should be no statistical association between the NCO and the treatment.
A possible confounding mechanism is health status and lifestyle, such as the people who are more healthy in general also tend to take the influenza vaccine.
In a similar example, when discussing the impact of air pollutants on asthma hospital admissions, Sheppard et al.[9] et al. used non-elderly appendicitis hospital admissions as NCO.
Shi et al.[3] presented formal conditions for a negative control outcome
If, however, during the influenza vaccine medical visit, the physician also performs a general physical test, recommends good health habits, and prescribes vitamins and essential drugs.
This violation would occur when we choose a poor NCO, that is not or very weakly correlated with the unmeasured confounders
