Online Program Home
My Program

Abstract Details

Activity Number: 358 - Contributed Poster Presentations: Section on Statistics in Epidemiology
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #304316
Title: A Comparison of Statistical Causal Inference Methods for Animal Health Applications
Author(s): Ju Ji* and Chong Wang and Zhulin He and Karen Hay and Tamsin Barnes and Annette O'Connor
Companies: Iowa State University and Iowa State University and Iowa State University and QIMR Berghofer Medical Research Institute and The University of Queensland and Iowa State University
Keywords: propensity score; IPW; ICPW; causal inference; double robust; BRD

The causal effect of an exposure in an observational study cannot be estimated directly if the confounding variables are not controlled. Inverse probability weighting (IPW) approach can be used to adjust for confounding variables and provide unbiased estimates of the exposure's causal effect. For cluster-structured data, there may be unmeasured cluster-level confounders, and inverse conditional probability weighting (ICPW) approach can provide robust estimation. Double robust estimation combines an outcome model with a model for the exposure (propensity score) to estimate causal effect of an exposure. If at least one of the two models are correctly specified, the estimator will be unbiased. In this paper, the usage of IPW, ICPW and double robust approaches will be illustrated with an application study (the effect of prior bovine viral diarrhea exposure on bovine respiratory disease). The results from the simulation study showed that the IPW, ICPW and double robust approaches would provide more accurate estimation of exposure effect.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program