Table 1.

Comparison of the different propensity score methods.

Characteristics	Matchinga	Stratificationb	Regressionc	IPTWd
Model dependence	Minimum	Minimum	High	Minimum
Application1	Easy	Easy	Easy	Complex
Overall transparency	High	High	Low	Medium
Easy to communicate	Yes	Yes	Not always	Not always
Design and analysis	Separated	Separated	Mixed	Separated
Easy to check balance	Yes	Yes	No	Yes
Requires unique assumption2	No	No	Yes	No
Excluded individuals from analysis3	Yes	No	No	Yes-No
Variance estimation	Not clear	Easy	Easy	Complex
Easy to interpret4	Not always	Yes	No	Often
”Propensity score paradox”	Sensitive	No	No	No
Estimand5	Often ATT	ATE, ATT	ATE	ATE, ATT
Time-varying confounding6	No	No	No	Yes
Multiple treatments	Possible	Complex	Complex	Easier
Multi-level treatment applications	Exist	Exist	None	Exist
Treatment effect modification	Easier	Complex	Easier	Complex

^aConstructs treated and untreated matched groups with similar propensity scores. ^bConstructs subgroups of treated and untreated individuals, often quintiles or deciles of PS. ^cPS is used, as a single summary of all covariates included in PS model, in regression model. ^dPSs are used as weights to create a pseudo-population in which exposure and measured covariates included in the treatment (PS) model are independent (Ali et al., 2016). ¹Estimation of stabilized weights as well as extension to time-varying treatment and confounding setting in MSMs framework can be complex (Ali et al., 2016). ²Requires correct specification of PS and outcome model, apart from the basic assumptions that there is positivity and no unmeasured confounding (Ali et al., 2016). ³Weight trimming excludes some individuals in the tails of the propensity score distribution. ⁴In PSM, when treated individuals are excluded, interpretation of the treatment effect may change, not just ATT and in Stratification, when there is treatment effect modification by the PS, in regression adjustment using PS, when non-collapsible effect measures such as odds ratios are used. ⁵Modification of the matching or weighting method enable to estimate either ATT or ATE. ⁶When time-varying confounder is affected by previous treatment, all the propensity score based methods fail to correctly control for the confounding bias including standard IPWs; however, MSMs using IPWs.