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Weighted estimation for confounded binary outcomes subject to misclassification
Author(s) -
Gravel Christopher A.,
Platt Robert W.
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7522
Subject(s) - covariate , estimator , statistics , logistic regression , confounding , computer science , estimation , inverse probability , econometrics , inverse probability weighting , consistency (knowledge bases) , identification (biology) , mathematics , artificial intelligence , posterior probability , bayesian probability , management , economics , botany , biology
In the presence of confounding, the consistency assumption required for identification of causal effects may be violated due to misclassification of the outcome variable. We introduce an inverse probability weighted approach to rebalance covariates across treatment groups while mitigating the influence of differential misclassification bias. First, using a simplified example taken from an administrative health care dataset, we introduce the approach for estimation of the marginal causal odds ratio in a simple setting with the use of internal validation information. We then extend this to the presence of additional covariates and use simulated data to investigate the finite sample properties of the proposed weighted estimators. Estimation of the weights is done using logistic regression with misclassified outcomes, and a bootstrap approach is used for variance estimation.