[gov3009-l] Applied Statistics - 2/24 - Jessica Myers Franklin

Aaron Kaufman aaronkaufman at fas.harvard.edu
Sun Feb 21 18:29:32 EST 2016


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Jessica
Myers Franklin*, Assistant Professor at Harvard Medical School
and Biostatistician at Brigham & Women's Hospital. She will be presenting
work entitled *Comparing Marginal Estimators of Propensity-Adjusted
Treatment Effects in Studies With Few Observed Outcome Events.*  Please
find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/2242016-jessie-myers-franklin-harvard-brigham-womens>
.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.

-- Aaron Kaufman

Title: Comparing marginal estimators of propensity-adjusted treatment
effects in studies with few observed outcome events

Abstract: Nonrandomized studies of treatments from electronic healthcare
databases are critical for producing the evidence necessary to making
informed treatment decisions, but often rely on comparing rates of events
observed in a small number of patients. In addition, a typical study
constructed from an electronic healthcare database, for example,
administrative claims data, requires adjustment for many, possibly
hundreds, of potential confounders. Despite the importance of maximizing
efficiency when there are many confounders and few observed outcome events,
there has been relatively little research on the performance of different
propensity score methods in this context. In this talk, I will describe and
compare a wide variety of propensity-adjusted estimators of the marginal
relative risk. In contrast to prior research that has focused on specific
statistical methods in isolation of other analytic choices, I instead
consider a method to be defined by the complete multi-step process from
propensity score modeling to final treatment effect estimation. I evaluate
methods via a “plasmode” simulation study, which creates simulated data
sets based on a real cohort study of 2 treatments constructed from
administrative claims data. Our results suggest a reconsideration of the
most popular approaches to propensity score adjustment in this context.
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