Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on October 5 (12:00 EST). Nima
Hejazi will present "Evaluating treatment efficacy in vaccine clinical
trials with two-phase designs using stochastic-interventional causal
effects."
<Where>
In-person: CGIS K354
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
In clinical trials randomizing participants to active vs. control
conditions and following study units until the occurrence of a primary
clinical endpoint, evaluating the efficacy of a quantitative exposure
(e.g., drug dosage, drug-induced biomarker activity) is often challenging,
as statistical innovations in causal inference have historically focused on
estimands compatible only with binary or categorical exposures.
Stochastic-interventional effects, which measure the causal effect
attributable to perturbing the exposure's natural (i.e., observed) value,
provide an interpretable solution. Unfortunately, their use in vaccine
efficacy trials requires extra care, for such trials measure immunologic
biomarkers – useful for understanding the mechanisms by which vaccines
confer protection or as surrogate endpoints in future trials – via
outcome-dependent two-phase sampling (e.g., case-cohort) designs. These
biased, outcome-dependent sampling designs have earned their popularity:
they circumvent the administrative burden of collecting potentially
expensive biomarker measurements on all study units without limiting
opportunities to detect important biomarkers that may be mechanistically
informative of the disease or infection process. We outline a
semiparametric biased sampling correction that allows for asymptotically
efficient inference on a causal vaccine efficacy measure defined by
contrasting assignments of study units to active vs. control while
simultaneously hypothetically shifting biomarker expression in the active
condition, yielding a causal dose-response analysis informative of
next-generation vaccine efficacy and useful for transporting efficacy from
a source pathogen strain (e.g., SARS-CoV-2 at outbreak) to variants of
concern (e.g., Omicron BA.4/BA.5). We present the results of applying this
approach in an analysis of the U.S. Government / COVID-19 Prevention
Network’s COVE (Moderna) COVID-19 vaccine efficacy clinical trial.
<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