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Causal inference for survival outcomes webinar series 

Defining and identifying estimands that can guide vaccine policies and clinical decisions is difficult, even in a randomised experiment. The effects of vaccines critically depend on features of the population of interest, such as the prevalence of infection, the number of vaccinated, and social behaviours. To mitigate the dependence on such characteristics, estimands (and study designs) that require conditioning or intervening on exposure to the infectious agent have been advocated. But a fundamental problem is that exposure status is often unavailable or difficult to measure, which has made it impossible to apply existing methodology to study vaccine effects that account for exposure status.  

Professor Mats Stensrud will present new results on this type of vaccine effects. Under plausible conditions, Professor Stensrud will  show that point identification of certain relative effects is possible even when the exposure status is unknown. Furthermore, he will present bounds on the corresponding absolute effects. These results are applied to estimate the effects of the ChAdOx1 nCoV-19 vaccine on SARS-CoV-2 disease (COVID-19) conditional on post-vaccine exposure to the virus, using data from a large RCT. 

Speaker 

, Tenure-Track Assistant Professor of Statistics, Chair of Biostatistics, Department of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Switzerland 

Please note that the recording link will be listed on this page when available