Volume 10 (2014)
Volume 7 (2011)
Volume 5 (2009)
Volume 4 (2008)
Volume 3 (2007)
Volume 2 (2006)
Volume 1 (2005)
Most Downloaded Articles
- An Introduction to Causal Inference by Pearl, Judea
- Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study by Xiao, Yongling/ Abrahamowicz, Michal and Moodie, Erica E. M.
- Survival Models in Health Economic Evaluations: Balancing Fit and Parsimony to Improve Prediction by Jackson, Christopher H/ Sharples, Linda D and Thompson, Simon G
- Sample Size Estimation for Repeated Measures Analysis in Randomized Clinical Trials with Missing Data by Lu, Kaifeng/ Luo, Xiaohui and Chen, Pei-Yun
- Model Checking with Residuals for g-estimation of Optimal Dynamic Treatment Regimes by Rich, Benjamin/ Moodie, Erica E. M./ Stephens, David A and Platt, Robert W
Double-Robust Estimators: Slightly More Bayesian than Meets the Eye?
1University of British Columbia
Citation Information: The International Journal of Biostatistics. Volume 8, Issue 2, Pages 1–15, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1349, January 2012
- Published Online:
Consider the simple setting of point exposure, outcome and confounding variables, all of which are discrete. As is well known, parametric modeling of outcome given exposure and confounders and also exposure given confounders can yield a double-robust estimator. This has the property of being consistent as long as at least one of the two specified models is correct. Such an estimator can also be cast as arising from a compromise between the parametric outcome model and a nonparametric or saturated outcome model. This brings to mind an alternate compromise based on Bayesian model averaging, and prompts comparisons between the double-robust method and the Bayesian method.