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Survival or time-to-event analysis is a key discipline in biostatistics, e.g., put to prominent use in trials on treatment of and vaccination against COVID-19. A defining characteristic is that participants have varying follow-up times and outcome status is not known for all individuals. This phenomenon is known as censoring. If time-to-event and time-to-censoring are entirely unrelated, it is rather easy to see that hazards remain identifiable from censored data, and hazard estimators may subsequently be transformed to recover probability statements. However, COVID-19 treatment (because of competing risks) and vaccination trials (because of event-driven censoring) are just two of the many examples where event and censoring times are related.

For further details and to register for this event, please visit the event page on VicBiostat’s website

Survival or time-to-event analysis is a key discipline in biostatistics, e.g., put to prominent use in trials on treatment of and vaccination against COVID-19. A defining characteristic is that participants have varying follow-up times and outcome status is not known for all individuals. This phenomenon is known as censoring. If time-to-event and time-to-censoring are entirely unrelated, it is rather easy to see that hazards remain identifiable from censored data, and hazard estimators may subsequently be transformed to recover probability statements. However, COVID-19 treatment (because of competing risks) and vaccination trials (because of event-driven censoring) are just two of the many examples where event and censoring times are related.

For further details and to register for this event, please visit the event page on VicBiostat’s website

Survival or time-to-event analysis is a key discipline in biostatistics, e.g., put to prominent use in trials on treatment of and vaccination against COVID-19. A defining characteristic is that participants have varying follow-up times and outcome status is not known for all individuals. This phenomenon is known as censoring. If time-to-event and time-to-censoring are entirely unrelated, it is rather easy to see that hazards remain identifiable from censored data, and hazard estimators may subsequently be transformed to recover probability statements. However, COVID-19 treatment (because of competing risks) and vaccination trials (because of event-driven censoring) are just two of the many examples where event and censoring times are related.

For further details and to register for this event, please visit the event page on VicBiostat’s website

Survival or time-to-event analysis is a key discipline in biostatistics, e.g., put to prominent use in trials on treatment of and vaccination against COVID-19. A defining characteristic is that participants have varying follow-up times and outcome status is not known for all individuals. This phenomenon is known as censoring. If time-to-event and time-to-censoring are entirely unrelated, it is rather easy to see that hazards remain identifiable from censored data, and hazard estimators may subsequently be transformed to recover probability statements. However, COVID-19 treatment (because of competing risks) and vaccination trials (because of event-driven censoring) are just two of the many examples where event and censoring times are related.

For further details and to register for this event, please visit the event page on VicBiostat’s website