Table S2.
Summary of the methodological pros and cons of four different types of PS methods
| Method | Methodology | Advantages | Disadvantages |
|---|---|---|---|
| Stratification | People are assigned to a stratum based upon their PS. Strata are typically defined by percentiles of the PS, eg, quintiles. Hence, within each stratum, treated and untreated people roughly share the same characteristics. A treatment effect is calculated within each stratum, and the overall effect is a weighted average across strata The typical approach estimates ATE7 | Simpler approach in comparison with matching and weighting Across strata, effects are measured |
Comparability of treatment groups must be checked for all strata Comparability of all strata may be difficult to obtain Potentially less efficient in removing differences between treatment groups5 Low number of strata may create residual confounding7 The range of PS values within strata may create residual confounding7 |
| Matching | For each treated person, one or more untreated person(s) with a comparable PS are selected. A comparable PS can be defined in different ways, eg, nearest neighbor or caliper width The typical approach estimates ATT7 | Potentially more efficient in providing comparable treatment groups5 | Treated people may not have a match with the untreated people, leading to biased results Only reasonable to use if the untreated-to-treated ratio is large |
| Covariate adjustment | An outcome regression model is used. As a minimum, the treatment and the PS must be included in the model as independent variables. Other variables may also be included | Simple approach: PS is used to balance treatment groups and is incorporated directly in an outcome regression model | Stronger assumptions than other methods7 In certain circumstances, it is not clear which effect is estimated5 No separation of study design and analysis5 |
| Inverse probability of treatment weighting | Weights are used to create a pseudo-population in which the characteristics are comparable across the treatment groups. Thus, weights are increased for those people who have received the treatment unexpectedly The typical approach estimates ATE7 | Potentially more efficient in providing comparable treatment groups5 | A setting involving treated people with a low PS (or untreated people with a high PS) will generate large weights and variances8 |
Abbreviations: PS, propensity score; ATE, average treatment effect for the population (both treated and untreated people); ATT, average treatment effect among treated people.