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Activity Number: 258 - SPEED: Causal Inference and Related Methodology
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 2:45 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #332732
Title: Using Validation Data to Adjust the Inverse Probability Weighting Treatment Effect Estimator for Misclassified Treatment
Author(s): Danielle Braun* and Corwin Zigler and Francesca Dominici and Malka Gorfine
Companies: Harvard T. H. Chan School of Public Health and Harvard T.H. Chan School of Public Health and Harvard T. H. Chan School of Public Health and Tel Aviv University
Keywords: Causal Inference; Comparative Effectiveness Research; IPW Estimator; Measurement Error; Propensity Score; Validation Data
Abstract:

The inverse probability weighting estimator (IPW) is widely used to estimate the treatment effect in observational studies in which patient characteristics might not be balanced by treatment group. The estimator assumes that treatment assignment is error-free, but, in reality, treatment assignment can be measured with error. This arises in the context of comparative effectiveness research using administrative data sources in which accurate procedural or billing codes are not always available. We show the bias introduced to the estimator when using error-prone treatment assignment, and propose an adjusted estimator using a validation study to eliminate this bias. Our proposed estimator is not only unbiased but also minimizes the variance. We illustrate our method on a comparative effectiveness study assessing surgical treatments among Medicare beneficiaries diagnosed with brain tumors. We use linked SEER-Medicare data as our validation data, and apply our method to Medicare Part A hospital claims data where treatment is based on ICD9 billing codes, which do not accurately reflect surgical treatment.


Authors who are presenting talks have a * after their name.

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