Abstract

Observational studies comparing groups or populations to evaluate services or interventions usually require case-mix adjustment to account for imbalances between the groups being compared. Simulation studies have, however, shown that case-mix adjustment can make any bias worse.

One reason this can happen is if the risk factors used in the adjustment are related to the risk in different ways in the groups or populations being compared, and ignoring this commits the “constant risk fallacy”.

Case-mix adjustment is particularly prone to this problem when the adjustment uses factors that are proxies for the real risk factors.

Interactions between risk factors and groups should always be examined before case-mix adjustment in observational studies.