An Investigation of the Significance of Residual Confounding Effect

Abstract

Background: Observational studies are commonly conducted in health research. However, due to their lack of randomization, the estimated associations between the outcome and the exposure can be affected by unmeasured confounding factors. It is important to determine how likely a significant association observed between an outcome variable and a noncausally related exposure may be introduced by residual confounding factors. Methods: A simulation approach is developed based on the sufficient cause model to test the likelihood of significant associations observed between a noncausally related exposure and the outcome. Results: Based on the estimates from all 500 replicates, the association between the exposure and the outcome is found to be significant in 386 (77%) replicates when all confounders (component causes) are controlled for in the model. However, when a subset of real component causes and some noncausal factors are controlled for in the model, the association between exposure and the outcome becomes significant in 487 (97%) replicates. Conclusion: Even when all confounding factors are known and controlled for using conventional multivariate analysis, the observed association between exposure and outcome can still be dominated by residual confounding effects. Therefore, an observed significant association apparently provides limited evidence for a causal relationship.