Claims that are based on “big data” (data from large databases) or “real world data” (routinely collected data) can be misleading. This is because routinely collected data often does not contain any information on so-called “confounders”.
Confounders are factors other than the intervention that can affect the outcome. If we do not have any information on these factors, it is difficult to determine if an effect is due to correlation or causation (see Correlation ≠ Causation card).
BEWARE of claims that are based on big datasets alone and do did not account for confounders.
REMEMBER: Do not assume that an association between a treatment and an outcome that is found using “big data” or “real world data” means that the treatment caused the outcome unless other reasons for the correlation have been ruled out.