Published on March 13, 2014 by

Dealing with multiple confounders in observational studies: beyond logistic regression

While traditional regression methods have been proven as a powerful research tool, no technique is suitable for all circumstances. Two common situations in which regression techniques alone are likely to produce biased results is when the outcome is rare and the number of measured confounders is large; and/or when important confounders are neglected.
Analysing briefly an example in which both circumstances are present simultaneously, this article shows how propensity score matching associated with Monte Carlo sensitivity analysis can be considered an interesting complement to traditional multivariate modelling.