Epidemiologist and Biostatistician best known for advancing methods for drawing causal inferences from complex observational studies
James M. Robins is an M.D. and the Mitchell L. & Robin LaFoley Dong Professor of Epidemiology and Professor of Biostatistics at the Harvard T. H. Chan School of Public Health. He is best known for the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures…
James M. Robins is an M.D. and the Mitchell L. & Robin LaFoley Dong Professor of Epidemiology and Professor of Biostatistics at the Harvard T. H. Chan School of Public Health. He is best known for the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments. He is the developer of the so-called ‘G Methods’. These methods include the estimated G –formula, inverse probability of treatment weighted and doubly robust estimators of static and dynamic marginal structural models, and doubly robust G-estimation of structural nested failure time and mean models. These methods are now in wide use, particularly in medical research. The usual approach to the estimation of the effect of a time-varying treatment or exposure on time to disease is to model the hazard incidence of failure at time t as a function of past treatment history using a time-dependent Cox proportional hazards model. Dr. Robins has shown the usual approach may be biased whether or not further adjusts for past confounder history in the analysis. Dr. Robins has applied his methods to analyze the effect of a non-randomized treatment aerosolized pentamidine on the survival of AIDS patients in ACTG Trial 002; the effect of arsenic exposure on the mortality experience of a cohort of Montana copper smelter workers; the effect of formaldehyde on the respiratory disease mortality of a cohort of U.S. chemical workers; and the effect of smoking cessation on subsequent myocardial infarction and death within the MRFIT randomized trial.
Ernie was perfect for our Elders Conference at Little River Casino Resort in Manistee, Michigan. We will definitely be using PDA again!
LITTLE RIVER BAND OF OTTAWA INDIANS
Our speaker was great and the crowd enjoyed her! She was very engaging. Thank you again PDA for the great list of suggestions!
Everything went great! and our speaker was wonderful! Thank you so much for all your help PDA! Looking forward to the next one.
UNIVERSITY OF MISSOURI – KANSAS CITY
I hope that this message finds you well. We had a phenomenal time with Atsuko this past Thursday. We wanted to just let you know how amazing she was. All those that were in attendance thoroughly enjoyed her and had nothing but amazing things to say about her. Thank you for working with us to make sure this happened!
BAYLOR UNIVERSITY CAMPUS
We had a fantastic day with Inge! She was very engaging with all those that attended. Her personal stories about the holocaust were very special and we were moved that she was willing to share them with everyone. Thank you again, PDA for all your hard work and dedication to make this program such a success!
SALT LAKE COMMUNITY COLLEGE
Dear PDA Group, Thank you so much for your hard work in scheduling our speaker! From the moment we reached out to you the first time, you worked with us on all details and logistics and kept us up-to-date on the progress. Thank you for all your hard work on making our event such a success! We couldn't have done it without you and your team!
Thank you again, Dr. Kimbrough, for a great presentation, our attendees are still talking about it!
Dr. Henry Lee's presentation was entertaining, insightful and wise. I, and everyone at Pfizer, especially appreciate the remarks you directed about following your passion and preparing yourself to excel. Thank you, everyone, at PDA for helping to make this happen!
The event was successful! I received many messages expressing how awesome, outstanding and inspiring Ms. Webb-Christburg's speech was."
DR. MARTIN LUTHER KING, JR. MEMORIAL BREAKFAST, BOSTON, MLK SPEAKER
Everything went well. Atsuko was very easy to work with and the students enjoyed her performance! Thank you again for your recommendation and I hope to work with you again in the future!
CENTRAL MICHIGAN UNIVERSITY
Dr. Novello left this morning after a very successful event on our campus! Thank you PDA Group for your help from the first phone to the last you were with us every step of the way.
GRAND VALLEY STATE UNIVERSITY
Jordan Carlos was very very entertaining! A majority of students who answered the event survey indicated they really enjoyed Jordan and definitely will bring him back!
WESTERN CAROLINA UNIVERSITY
On behalf of myself and my organization Entertainment Unlimited the Campus Programming Board of Ferris State University, we thank you PDA and Dan for a successful event!! Dan was magnificent! Students really enjoyed the presentation. I know I did. Can't wait to bring in more programs with PDA Group.
FERRIS STATE UNIVERSITY
It was a great pleasure to work with PDA Group! The communication was always prompt, friendly and helpful. We loved the speakers and events we booked through Mr. Peter Walker (PDA Group).
WESTERN MICHIGAN UNIVERSITY
Thank you, Peter, for all your help, you'll be happy to know that the event went incredibly well! Aneesa Ferreira was by far one of the best speakers we've ever had!
Causal inference, machine learning, and optimal decisions
The optimal treatment, e.g. the best drug to prescribe, or action , e.g. the best online add to present to a consumer, depend on the past medical or purchasing history of patients and consumers. In his talk, he will discuss how to use high dimensional data to estimate the optimal treatment or action for a given individual based on data collected on their past history either from observational, non-experimental data, or from a combination of randomized assignment (AB testing ) data and non-experimental data. He will describe why these causal methods are needed and, through case studies, show how much better they perform in practice compared to purely association machine learning methods.
April 24, 2019 Seminar Series: James Robins
1 HOUR OR LESS