Abstract: Randomized controlled trials are the gold standard approach for estimating causal effects of treatments on health outcomes, but are typically restricted to relatively short follow-up time and a subset of the eventual treatment population. Observational data on treatment use offers the possibility of estimating treatment effects over long periods of follow-up and in diverse populations. However, to estimate treatment effects from observational data we must tackle the challenge of confounding, especially by time-dependent covariates. This talk will explore methods for estimating the effects of treatment on survival using longitudinal observational data, with a particular emphasis on how we can create a ‘target trial’ within an observational cohort. I am motivated by the aim of estimating the effect of a treatment used in cystic fibrosis on survival using data from the UK Cystic Fibrosis Registry.
This event is part of the Sociology Seminar Series.