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Our recent article (with lead author Ashley Naimi) describes the usage of causal mediation methods to evaluate potential interventions to reduce health disparities between racial groups in the US. In this article, Dr. Naimi describes how elevated non-Hispanic Black infant mortality rates can be largely (but not entirely) explained by lower rates of breastfeeding prior to hospital discharge. However, the importance of this potential intervention is seemingly overestimated using standard regression methods compared to double robust approaches.

I was happy to participate in this very interesting project, which is currently in press at the American Journal of Epidemiology.

In November I gave a seminar for the Canadian Network for Observational Drug Effect Studies (CNODES) entitled

"Targeted Learning for the Estimation of Drug Safety and Effectiveness: Getting Better Answers by Asking Better Questions."

​The video presentation synced with the overhead slides is now online on the CNODES website.

In this talk I demonstrate the importance of taking a marginal approach to the estimation of the effects of medications. I focus on the "Targeted Learning" roadmap (van der Laan and Rose, 2011) which is used to construct robust estimators of causal effects.

Photo credit: Christina Esteban Photography, http://cepstudio.com

Our article, entitled 
"Double robust and efficient estimation of a prognostic model for events in the presence of dependent censoring"

has been published in the journal Biostatistics and is now available on Pub Med at http://www.ncbi.nlm.nih.gov/pubmed/26224070 .

This article presents two simple double robust estimators of a mean ("marginal") model when subjects are longitudinally censored. In our example, we were interested in modeling the probability of negative health outcomes in subjects with HIV under the scenario where all subjects remained on treatment that successfully controlled viral load. Subjects were artificially censored when they did not continue on treatment, which therefore required modeling adjustment.

In an effort to make double robust computation more accessible, we provide code for fitting similar models (with example usage). Thanks to the referees and the journal editors for encouraging open access code!