Table 3

Bradford-Hill indicators of causality, showing the importance of both probabilistic evidence (eg, from RCTs) and mechanistic evidence (eg, from laboratory studies)

IndicatorExplanationTypePreferred types of evidence
Strength of associationA strong association is more likely to be causal than a weak oneBothRCTs or unbiased observational studies (eg, longitudinal cohort) which allow calculation of a measure of association such as relative risk or OR, and adjustment for confounding.
ConsistencyMultiple observations made by different observers with different instruments mean the association is more likely to be causalBothSystematic review. Exploration of heterogeneity via tools such as meta-regression may help identify and quantify inconsistency in effects across studies.
SpecificityIf an outcome is best predicted by one primary factor, the causal claim is more credibleNeitherA problematic indicator, as it is common for a single exposure to be causally associated with multiple outcomes (eg, tobacco smoking and heart disease, stroke, cancer, etc).
TemporalityA cause must precede an effectBothAny longitudinal design for example, clinical trial, non-cross-sectional observational study.
Biological gradientThere should be a direct ‘dose-response’ relationship between the independent variable (eg, a risk factor) and the dependent one (eg, people’s status on the disease variable)BothBasic science (eg, toxicology-based risk analysis), varying-dose RCTs, unbiased observational studies with adjustment for confounding.
PlausibilityAn association is more likely to be causal if there is a rational and theoretical basis for itMechanisticBasic science
CoherenceAn association is more likely to be causal if it coheres with other knowledge (ie, does not conflict with what is known about the variables under study and there are no plausible competing theories or rival hypotheses)BothSystematic review; integration of both mechanistic AND probabilistic evidence is key here.
Experimental manipulationAny related research that is based on experiments will make a causal inference more plausibleBothBasic science or RCT (which may be considered ‘epidemiological experiments’, as exposure is defined by the investigator).
AnalogySometimes a commonly accepted phenomenon in one area can be applied to another areaBothRequires a broad understanding of all relevant fields; potentially subject to logical fallacy.
  • Adapted from Hill and Bradford Hill,15 incorporating important subsequent insights from Russo and Williamson16 and Fedak et al. 17

  • RCT, randomised controlled trial.