Augmenting treatment arms with external data through propensity-score weighted power-priors with an application in expanded access 199 9♥ 0.0 0.1 0.2 0.3 -0.50 -0.25 0.00 0.25 0.50 η (drift ) Type I error rate A Drift 1. Equal sample size:N0 =Ne 0.03 0.04 0.05 0.06 -0.50 -0.25 0.00 0.25 0.50 η (drift ) RMSE B Drift 1. Equal sample size:N0 =Ne 0.0 0.1 0.2 0.3 -0.50 -0.25 0.00 0.25 0.50 η (drift ) Type I error rate C Drift 2. Larger external data:N0 × 5 =Ne 0.025 0.050 0.075 0.100 -0.50 -0.25 0.00 0.25 0.50 η (drift ) RMSE D Drift 2. Larger external data:N0 × 5 =Ne 0.0 0.1 0.2 0.3 -0.50 -0.25 0.00 0.25 0.50 η (drift ) Type I error rate E Drift 3. Larger current data:N0 = 2 ×Ne 0.030 0.035 0.040 0.045 -0.50 -0.25 0.00 0.25 0.50 η (drift ) RMSE F Drift 3. Larger current data:N0 = 2 ×Ne 0.0 0.1 0.2 0.3 -0.50 -0.25 0.00 0.25 0.50 η (drift ) Type I error rate G Drift 3. More covariates: |X| = 10 0.03 0.04 0.05 0.06 -0.50 -0.25 0.00 0.25 0.50 η (drift ) RMSE H Drift 3. More covariates: |X| = 10 Method Ignore Pooling Pro P M P Wang 10% Wang 20% Figure 3: Comparison of different estimation methods in terms of type I error (left) and root mean squared error (RMSE) (right) when the difference in outcomes is in part driven by a random drift term. There is no difference in covariates (Setting 1).
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