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 Medjournal_title
Statistics in medicineauthors
Gravel CA,Platt RWdoi
10.1002/sim.7522subject
Has Abstractpub_date
2018-02-10 00:00:00pages
425-436issue
3eissn
0277-6715issn
1097-0258journal_volume
37pub_type
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