Estimating subgroup effects using the propensity score method: a practical application in outcomes research.

Objective: Our aim was to demonstrate the feasibility of the univariate and generalized propensity score (PS) method in subgroup analysis of outcomes research.

Methods: First, to estimate subgroup effects, we tested the performance of 2 different PS methods, using Monte Carlo simulations: (1) the univariate PS with additional adjustment on the subgroup; and (2) the generalized PS, estimated by crossing the treatment options with a subgroup variable. The subgroup effects were estimated in a linear regression model using the 2 PS adjustments. We further explored whether the subgroup variable should be included in the univariate PS. Second, the 2 methods were compared using data from a large effectiveness study on psychotherapy in personality disorders. Using these data we tested the differences between short-term and long-term treatment, with the severity of patients' problems defining the subgroups of interest.

Results: The Monte Carlo simulations showed minor differences between both PS methods, with the bias and mean squared error overall marginally lower for the generalized PS. When considering the univariate PS, the subgroup variable can be excluded from the PS estimation and only adjusted for in the outcome equation. When applied to the psychotherapy data, the univariate and generalized PS estimations gave similar results.

Conclusion: The results support the use of the generalized PS as a feasible method, compared with the univariate PS, to find certain subgroup effects in nonrandomized outcomes research.