Background and objective: To review methods that seek to adjust for confounding in observational studies when assessing intended drug effects.
Methods: We reviewed the statistical, economical and medical literature on the development, comparison and use of methods adjusting for confounding.
Results: In addition to standard statistical techniques of (logistic) regression and Cox proportional hazards regression, alternative methods have been proposed to adjust for confounding in observational studies. A first group of methods focus on the main problem of nonrandomization by balancing treatment groups on observed covariates: selection, matching, stratification, multivariate confounder score, and propensity score methods, of which the latter can be combined with stratification or various matching methods. Another group of methods look for variables to be used like randomization in order to adjust also for unobserved covariates: instrumental variable methods, two-stage least squares, and grouped-treatment approach. Identifying these variables is difficult, however, and assumptions are strong. Sensitivity analyses are useful tools in assessing the robustness and plausibility of the estimated treatment effects to variations in assumptions about unmeasured confounders.
Conclusion: In most studies regression-like techniques are routinely used for adjustment for confounding, although alternative methods are available. More complete empirical evaluations comparing these methods in different situations are needed.