Table 1.

Advantages and disadvantages of common strategies used to address confounding

Method	Overview	Advantages	Disadvantages
Design phase
Restriction	Setting criteria for study inclusion	Easy to implement	Only removes or reduces confounding by the inclusion criteria Reduces sample size Cannot generalize findings to those excluded
Matching	Creates matched sets of patients who have similar values of one or more confounders	Intuitive	Difficult to match on multiple confounders Only removes or reduces confounding by the matching factors Unmatched patients are excluded, reducing sample size, effect estimate precision, and generalizability
Active comparator	Comparing the treatment of interest to an active comparator rather than treatment nonuse	Mitigates confounding by indication Clinically relevant head-to-head comparison of two or more treatments	Cannot be used when there is only one treatment option
Analysis phase
Multivariable adjustment	Potential confounders are included as covariates in regression models	Easy to implement in standard statistical software packages	Only controls for measured confounders The total number of confounders that can be included in regression models is contingent on the number of outcome events
Propensity score matching	Each patient who received the treatment of interest is matched to one or more patients who received the comparator treatment with an equivalent propensity score, generating a matched cohort of treated and comparator patients that have similar baseline characteristics	Preferred in studies where there are relatively few outcome events compared with the number of potential confounders Ability to check if covariate balance between the treated and comparator groups was achieved in the matched cohort	Only controls for measured confounders Unmatched patients are excluded, reducing sample size, effect estimate precision, and generalizability
Propensity score weighting	The propensity score is used to generate weights that are applied to the original study cohort to create a pseudo-population of treated and comparator patients that have similar baseline characteristics	Preferred in studies where there are relatively few outcome events compared with the number of potential confounders Ability to check if covariate balance between the treated and comparator groups was achieved in the weighed cohort	Only controls for measured confounders Less intuitive than propensity score matching
G methods	Complex analytic methods that handle time-varying confounding in the setting of time-varying exposures	Appropriately handle time-varying confounding	Only controls for measured confounders Complex methods requiring advanced statistical expertise