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Comparing the estimates of effect obtained from statistical causal inference methods: An example using bovine respiratory disease in feedlot cattle
- Source :
- PLoS ONE, Vol 15, Iss 6, p e0233960 (2020), PLoS ONE
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
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.
- Subjects :
- Epidemiology
0403 veterinary science
0302 clinical medicine
Statistics
Medicine and Health Sciences
Odds Ratio
Public and Occupational Health
030212 general & internal medicine
Mathematics
Mammals
Multidisciplinary
Simulation and Modeling
Inverse probability weighting
Confounding
Eukaryota
Conditional probability
Confounding Factors, Epidemiologic
Regression analysis
Ruminants
04 agricultural and veterinary sciences
Vaccination and Immunization
Veterinary Diseases
Vertebrates
Medicine
Bovine Virus Diarrhea-Mucosal Disease
Research Article
Biometry
040301 veterinary sciences
Science
Immunology
Bovine Respiratory Disease Complex
Research and Analysis Methods
Veterinary Epidemiology
03 medical and health sciences
Bias
Bovines
Covariate
Animals
Computer Simulation
Estimation
Models, Statistical
Organisms
Australia
Biology and Life Sciences
Weighting
Medical Risk Factors
Causal inference
Amniotes
Veterinary Science
Cattle
Preventive Medicine
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....246642613f0dd1b591527ff4ab2eb7b7