On 28th November OxTalks will move to the new Halo platform and will become 'Oxford Events' (full details are available on the Staff Gateway).
There will be an OxTalks freeze beginning on Friday 14th November. This means you will need to publish any of your known events to OxTalks by then as there will be no facility to publish or edit events in that fortnight. During the freeze, all events will be migrated to the new Oxford Events site. It will still be possible to view events on OxTalks during this time.
If you have any questions, please contact halo@digital.ox.ac.uk
Observational data, from electronic health records, claim databases or disease registries, are increasingly used to understand the causal effect of treatments or exposures on health outcomes. When the start of follow-up, the time of eligibility assessment and the time of treatment initiation do not coincide, standard analysis methods may lead to biased estimates of treatment effects. This bias is known as immortal-time bias. In this presentation, we will first describe situations in which this bias occurs, and then introduce a few solutions to prevent or handle this issue, with a focus on the clone-censor-weight approach.
