Using Inverse Probability Weighting Estimators to Evaluate Various Propensity Scores When Treatment Switching Exists

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 treatme...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 45; no. 6; pp. 2182 - 2190
Main Authors Tu, Chunhao, Koh, Woon Yuen
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.07.2016
Taylor & Francis Ltd
Subjects
Adjustment
Average treatment effect
Computer simulation
Economic models
Estimators
Expected utility
Inverse
Monte Carlo methods
Monte Carlo simulation
Observational studies
Probability
Propensity score
Standard error
Switching
Treatment switching
Weighting
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Summary: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.
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2014.894058