Hi everyone,
This week at the Applied Statistics Workshop we will be welcoming Francesca Dominici, a
Professor of Biostatistics and Senior Associate Dean for Research at the Harvard T.H. Chan
School of Public Health. She will be presenting work entitled "Model Averaged Double
Robust Estimation." Please find the abstract below and on the
website<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/ho…me>. In
addition, the paper is attached.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided. See you all
there!
-Pam
Model averaged double robust estimation
Francesca Dominici, Harvard T H Chan School of Public Health
Joint with: Matt Cefalu, Giovanni Parmigiani, Nils Arvold.
ABSTRACT. Researchers are increasingly being challenged with decisions on how to best
control for a high-dimensional set of potential confounders when estimating causal
effects. Typically, a single propensity score model is used to adjust for confounding,
while the uncertainty surrounding the procedure to arrive at this propensity score model
is often ignored and failure to include even one important confounder will results in
bias. We propose a general causal framework that overcomes the limitations described above
through the use of model averaging. We illustrate the proposed framework in the context of
double robust estimation.The MA-DR estimator is defined as a weighted average of double
robust estimators, where each double robust estimator corresponds to a specific choice for
the outcome model and the propensity score. The MA-DR estimator extend the desirable
double robustness property by achieving consistency under the much weaker assumption that
either the true propensity score model or the true outcome model be within a specified,
possibly large, class of models. Importantly, using simulation studies, we found that our
MA-DR estimator dramatically reduces mean squared error by the largest percentage in the
realistic situation where the set of potential confounders is large relative to the sample
size. We apply the methodology to estimate the comparative effectiveness of the oral
chemotherapy temozolomide on 1-year survival in a cohort of 1887 Medicare enrollees who
were diagnosed with glioblastoma between June 2005 and December 2009.