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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20211005T134215Z
DTSTART:20211027T130000Z
DTEND:20211027T140000Z
SUMMARY:Jonathan Bartlett - Hypothetical estimands in clinical trials -
a unification of causal inference and missing data methods
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}cjk-kty8d7k
8-vaql17
DESCRIPTION:Jonathan Bartlett\, Reader in Statistics at the Department of
Mathematical Sciences at the University of Bath is our speaker for the
Statistics seminar series.\n\nTitle: Hypothetical estimands in clinical
trials - a unification of causal inference and missing data methods\n\nA
bstract: 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 th
e effect that would have been seen had rescue medication not been availa
ble may be of interest to some stakeholders. In this talk I will discuss
statistical methods for estimation of such hypothetical estimands. I wi
ll first describe hypothetical estimands using the causal inference conc
epts of potential outcomes\, before using the existing causal inference
machinery to describe what assumptions are needed to estimate hypothetic
al estimands. In particular this will allow us to be clear about what va
riables need to be adjusted for to estimate hypothetical estimands. I wi
ll then discuss both ‘causal inference’ and ‘missing data’ methods (such
as mixed models) for estimation\, and show that in certain situations e
stimators from these two sets are in fact identical. These links may hel
p those familiar with one set of methods but not the other. They may als
o identify situations where currently adopted estimation approaches may
be relying on unrealistic assumptions\, and suggest alternative approach
es for estimation.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Zoom link: https://zoom.us/j/92947173491
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