Weighted estimation for confounded binary outcomes subject to misclassification

Weighted estimation for confounded binary outcomes subject to misclassification

Overview

Description

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.

Manuscripts

Gravel C, Platt RW. Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med. 2018 Feb 10;37(3):425-436.

Presentations

Project Team

Project Lead
Christopher Gravel PhD
CPRD
Collaborator
Robert W. Platt PhD
CPRD