Dear Applied Statistics Workshop Community,

Our next meeting of the semester will be on November 16 (12:00 EST). Iván Diaz will present "Causal survival analysis under competing risks using longitudinal modified treatment policies."

<Where>
Hybrid: CGIS K354 or Zoom (the presenter will join us via Zoom)
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom: https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09

<Abstract>
Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, numerical, or continuous exposures measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to right-censoring and competing risks. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as root-n-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.

<2022 Schedule>
GOV 3009 Website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar: https://calendar.google.com/calendar/embed?src=c_3v93pav9fjkkldrbu9snbhned8%40group.calendar.google.com&ctz=America%2FNew_York

Best,
Shusei