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…
Best,
Shusei