Table 1.

Summary of Strengths and Weaknesses of Methods to Address Confounding by Indication^a

Method	Strengths	Weaknesses
Restriction	Easy to do, easy to understand	Can limit power
Matching	Easy to do, easy to understand	Difficult to match on a large number of variables
Stratification	More powerful than restriction and matching	Difficult to deal with strata with small numbers. Can introduce residual confounding if subjects with very different values of a factor are grouped into a strata (eg, quartiles)
Multivariable regression	More powerful than restriction and matching	Model building can be complicated Can introduce residual confounding if subjects with very different values of a factor are grouped into a strata (eg, quartiles)
Propensity scores	Can be used to assess amount of unmeasured confounding that exists	Not designed specifically to deal with unmeasured confounding
Instrumental variables	One of the newer, more powerful methods	Difficulty in finding the optimal instrumental variable

^a All methods cannot control for unmeasured confounding.