Linked from

The 46 pages that link to Confounding, each with the reason it gives.

• Evidence-based medicineRelated: It can make an ineffective treatment appear beneficial, or obscure a real effect.

• Causal inferenceRelated: It creates noncausal associations that can be mistaken for treatment effects.

• EpidemiologyRelated: It can make an apparent determinant look causal when another factor explains the association.

• CausalityRelated: Confounding can make an association appear causal when a third factor drives both variables.

• ComorbidityRelated: A shared cause can make two conditions appear causally linked when they are not.

• PharmacovigilanceRelated: Underlying illness and other medicines can make a drug appear responsible for an event.

• Experimental designRelated: Uncontrolled differences between groups can make an apparent treatment effect misleading.

• Field experimentRelated: Randomization is used to reduce confounding in comparisons between field conditions.

• Observational studyRelated: A confounder can make an observed association differ from the causal effect.

• Simpson's paradoxRelated: A confounder can shape group membership and outcomes, producing the reversal.

• BiomonitoringRelated: Biological measurements alone cannot establish that a pollutant caused an observed health effect.

• Healthcare-associated infectionRelated: Patients' underlying illness can complicate comparisons of infection rates across facilities.

• Risk factorRelated: A confounder can make a noncausal factor appear linked to disease.

• Natural experimentRelated: External assignment is valuable when it reduces confounding between exposure and outcome.

• Design of experimentsRelated: Poorly chosen treatment combinations can make factor effects inseparable.

• Quasi-experimentRelated: Nonrandom treatment assignment can make confounding a central threat.

• ExperimentRelated: An uncontrolled factor can make a treatment appear to cause an effect it did not.

• Instrumental variableRelated: Instrumental-variable designs aim to recover effects despite exposure–outcome confounding.

• Nutritional epidemiologyRelated: Dietary patterns cluster with behaviors and social conditions that also affect health.

• Hawthorne effectRelated: Attention, incentives, and changing conditions can be mistaken for effects of observation.

• AnticholinergicRelated: Underlying illnesses may influence both anticholinergic prescribing and cognitive outcomes.

• Control groupRelated: Confounders can make group differences appear to result from the intervention.

• Experimental methodRelated: Confounders can make an intervention appear effective for the wrong reason.

• Veterinary epidemiologyRelated: Can make an apparent animal disease risk factor misleading.

• BiostatisticsRelated: Confounding can make observational biomedical associations misleading about causal effects.

• Clinical researchRelated: Confounding can make observational findings misleading about treatment effects.

• Dietary guidelinesRelated: It complicates claims that particular foods or nutrients cause health outcomes.

• Suicide contagionRelated: Concurrent social conditions can make media exposure appear more causal than it is.

• Nutritional scienceRelated: Lifestyle and social factors can make diet-disease associations hard to interpret.

• Random assignment (experimental design)Related: Random assignment reduces confounding by making baseline factors independent of treatment in expectation.

• Dental abrasionRelated: Erosion, brushing force, and diet can obscure abrasion's independent contribution to lesions.

• Andrew WakefieldRelated: Without an appropriate comparison, reported timing could not establish that vaccination caused developmental symptoms.

• James LindRelated: Small groups and unequal treatments limit how confidently the trial isolates citrus’s effects.

• Matching (statistics)Related: Matching aims to reduce confounding from measured characteristics.

• Richard DollRelated: Study design and analysis had to address alternative explanations for smoking’s association with cancer.

• Austin Bradford HillRelated: An apparent causal relationship can arise because the observed association is confounded.

• Correlation does not imply causationRelated: A common cause can make two variables move together without either causing the other.

• Herbert NeedlemanRelated: Socioeconomic conditions could affect both lead exposure and test performance, making careful analysis essential.

• Digital media use and mental healthRelated: Pre-existing distress or life circumstances can influence both media use and mental health.

• Psychological researchRelated: Confounds can make an apparent psychological effect misleading.