Weighted estimation for confounded binary outcomes subject to misclassification.

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Gravel CA,Platt RW

doi

10.1002/sim.7522

subject

Has Abstract

pub_date

2018-02-10 00:00:00

pages

425-436

issue

3

eissn

0277-6715

issn

1097-0258

journal_volume

37

pub_type

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