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Activity Number: 497
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #320802 View Presentation
Title: Weighted Estimation in Confounded Binary Data Subject to Outcome Misclassification
Author(s): Christopher A. Gravel* and Robert W. Platt
Companies: McGill University and McGill University
Keywords: outcome misclassification ; internal validation data ; inverse probability weighting ; Monte Carlo sample size determination ; administrative claims data

We consider a study with a binary exposure and outcome, subject to confounding and misclassification of the outcome variable. There are several methods to adjust for misclassification of exposure; however, misclassification of outcome remains relatively unexplored. Outcomes from administrative claims data are often subject to misclassification, as diagnoses are based on coding such as ICD-10, which may not always reflect true outcomes. We use inverse probability weighting and internal validation sampling to rebalance covariates across treatment groups while mitigating misclassification bias. We discuss several validation sampling schemes and a Monte Carlo approach to approximate optimal sample size determination. A parametric bootstrap is used for variance estimation. We explore finite sample properties of the weighted estimators via simulation, with particular attention to relative efficiency of different sampling schemes for validation. We demonstrate the use of the methods through an example using administrative data.

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

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