Table 1. Properties of the four different propensity score (PS) methods and of conventional regression analysis in evaluating non-randomized treatment effects.
| Method | |||||
|---|---|---|---|---|---|
| PS method | Conventional regression analysis | ||||
| PS matching | IPTW estimation | Stratification | Regression adjustment for the PS | ||
| Allows for easy assessment of comparability of treated and untreated patients | + | (+) | (+) | − | − |
| Allows assessment of balance of characteristics in the data | + | + | (+) | − | − |
| Uses complete dataset (smaller variance of the treatment effect. greater danger of bias) | − | + | + | + | + |
| Similar to an RCT (generates comparable groups. ignores outcomes) | + | (+) | (+) | − | − |
| Robust against outliers (patients with extreme propensity scores) | + | − | + | + | + |
| Fewer statistical assumptions in the model | + | + | (+) | − | − |
RCT. randomized controlled trial; IPTW. inverse probability of treatment weighting; PS. propensity score_
“+” stands for “yes”; “-” stands for “no”; “(+)” stands for “partially given”