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In this paper, we conduct a Monte Carlo simulation study to evaluate three propensity score (PS) scenarios for estimating an average treatment effect (ATE) in observational studies when treatment switching exists: (a) ignoring treatment switching in subjects (UPS), (b) removing subjects with treatment switching (RPS), and (c) adjusting for treatment switching effect (APS) with two inverse probability weighting estimators, IPW1 and IPW2. We evaluate these six estimators in terms of bias, mean squared error (MSE), empirical standard error (ESE), and coverage probability (CP) under various simulation scenarios. Simulation results show that the IPW2 estimator with RPS has relatively good performance. [ABSTRACT FROM AUTHOR]

The authors focus discussion on estimation of the proportion ratio (PR) in the presence of residual effects under the AB/BA design. Under a randomeffects multiplicative risk model,we develop three point estimators and three interval estimators accounting for residual effects for the PR. Using Monte Carlo simulations, we compare the performance of these point (or interval) estimators with point (or interval) estimators assuming no residual effects with respect to the bias and mean-squared-error (or the coverage probability and average length). The authors use the data taken from an AB/BA trial comparing two new inhalation devices to illustrate the use of these estimators. [ABSTRACT FROM AUTHOR]