Abstract:
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Propensity score methods are widely used to analyze observational studies in which patient characteristics might not be balanced by treatment group. These methods assume that both treatment assignment and confounders, are error-free, but in reality these variables can be subject to measurement error. This arises in the context of comparative effectiveness research, using observational administrative claims data in which accurate information is not always available. When using propensity score based methods, this error affects both the treatment assignment variable directly, as well as the propensity score. We extend previous work adjusting for error in the treatment assignment variable only, to this setting in which both treatment assignment and confounders are subject to measurement error. We propose a multiple imputation approach using validation data to adjust for the measurement error.
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