Campus Units

Veterinary Diagnostic and Production Animal Medicine, Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

6-25-2020

Journal or Book Title

PLoS ONE

Volume

15

Issue

6

First Page

e0233960

DOI

10.1371/journal.pone.0233960

Abstract

The causal effect of an exposure on an outcome of interest in an observational study cannot be estimated directly if the confounding variables are not controlled. Many approaches are available for estimating the causal effect of an exposure. In this manuscript, we demonstrate the advantages associated with using inverse probability weighting (IPW) and doubly robust estimation of the odds ratio in terms of reduced bias. IPW approach can be used to adjust for confounding variables and provide unbiased estimates of the exposure’s causal effect. For cluster-structured data, as is common in animal populations, inverse conditional probability weighting (ICPW) approach can provide a robust estimation of the causal effect. Doubly robust estimation can provide a robust method even when the specification of the model form is uncertain. In this paper, the usage of IPW, ICPW, and doubly robust approaches are illustrated with a subset of data with complete covariates from the Australian-based National Bovine Respiratory Disease Initiative as well as simulated data. We evaluate the causal effect of prior bovine viral diarrhea exposure on bovine respiratory disease in feedlot cattle. The results show that the IPW, ICPW and doubly robust approaches would provide a more accurate estimation of the exposure effect than the traditional outcome regression model, and doubly robust approaches are the most preferable overall.

Comments

This article is published as Ji, Ju, Chong Wang, Zhulin He, Karen E. Hay, Tamsin S. Barnes, and Annette M. O’Connor. "Comparing the estimates of effect obtained from statistical causal inference methods: An example using bovine respiratory disease in feedlot cattle." PLoS ONE 15, no. 6 (2020): e0233960. DOI: 10.1371/journal.pone.0233960. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Ji et al.

Language

en

File Format

application/pdf

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