Jonathan Bartlett - Hypothetical estimands in clinical trials - a unification of causal inference and missing data methods
|Dates:||27 October 2021|
|Times:||14:00 - 15:00|
|What is it:||Seminar|
|Organiser:||Department of Mathematics|
|Who is it for:||University staff, External researchers, Current University students|
Jonathan Bartlett, Reader in Statistics at the Department of Mathematical Sciences at the University of Bath is our speaker for the Statistics seminar series.
Title: Hypothetical estimands in clinical trials - a unification of causal inference and missing data methods
Abstract: In diabetes trials some patients may require rescue medication during follow-up. If the level of rescue medication use differs between treatment groups, a treatment policy / intention to treat analysis may be difficult to interpret. Here a hypothetical estimand which targets the effect that would have been seen had rescue medication not been available may be of interest to some stakeholders. In this talk I will discuss statistical methods for estimation of such hypothetical estimands. I will first describe hypothetical estimands using the causal inference concepts of potential outcomes, before using the existing causal inference machinery to describe what assumptions are needed to estimate hypothetical estimands. In particular this will allow us to be clear about what variables need to be adjusted for to estimate hypothetical estimands. I will then discuss both ‘causal inference’ and ‘missing data’ methods (such as mixed models) for estimation, and show that in certain situations estimators from these two sets are in fact identical. These links may help those familiar with one set of methods but not the other. They may also identify situations where currently adopted estimation approaches may be relying on unrealistic assumptions, and suggest alternative approaches for estimation.
Organisation: University of Bath
Travel and Contact Information
Zoom link: https://zoom.us/j/92947173491