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X-WR-CALNAME:MRC Biostatistics Unit
X-ORIGINAL-URL:https://www.mrc-bsu.cam.ac.uk
X-WR-CALDESC:Events for MRC Biostatistics Unit
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DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20200924T140000
DTEND;TZID=UTC:20200924T150000
DTSTAMP:20200923T235715
CREATED:20200826T081637Z
LAST-MODIFIED:20200826T081637Z
UID:19096-1600956000-1600959600@www.mrc-bsu.cam.ac.uk
SUMMARY:BSU Seminar: 'Assumption-lean inference for generalised linear model parameters'
DESCRIPTION:Virtual BSU Seminar \nSpeaker: Prof Stijn Vansteelandt\, Ghent University \nTitle: ‘Assumption-lean inference for generalised linear model parameters’ \nAbstract: Inference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory because the chosen model is usually the result of a data-adaptive model selection process\, which induces bias and excess uncertainty that is not usually acknowledged; moreover\, the assumptions encoded in the resulting model rarely represent some a priori known\, ground truth. Standard inferences may therefore lead to bias in effect estimates\, and may moreover fail to give a pure reflection of the information that is contained in the data. Inspired by developments on assumption-free inference for so-called projection parameters\, we here propose nonparametric definitions of main effect estimands and effect modification estimands. These reduce to standard main effect and effect modification parameters in generalised linear models when these models are correctly specified\, but continue to capture the primary (conditional) association between two variables\, or the degree to which two variables interact (in a statistical sense) in their effect on outcome\, even when these models are misspecified. We achieve an assumption-lean inference for these estimands by deriving their influence curve under the nonparametric model and invoking flexible data-adaptive (e.g.\, machine learning) procedures. This talk aims to be broadly accessible\, focussing on concepts more than technicalities. \n\nTo find out how to attend the seminar via MS Teams\, please contact alison.quenault@mrc-bsu.cam.ac.uk
URL:https://www.mrc-bsu.cam.ac.uk/event/bsu-seminar-assumption-lean-inference-for-generalised-linear-model-parameters/
LOCATION:Virtual Seminar via MS Teams
CATEGORIES:BSU seminars,Seminar
ORGANIZER;CN="MRC%20Biostatistics%20Unit":MAILTO:research_admin@mrc-bsu.cam.ac.uk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20201013T140000
DTEND;TZID=UTC:20201013T150000
DTSTAMP:20200923T235715
CREATED:20200828T152341Z
LAST-MODIFIED:20200828T152341Z
UID:19131-1602597600-1602601200@www.mrc-bsu.cam.ac.uk
SUMMARY:BSU Seminar: 'Stochastic treatment interventions in causal survival analysis'
DESCRIPTION:Virtual BSU Seminar \nSpeaker: Dr Lan Wen\, Harvard University \nTitle: ‘Stochastic treatment interventions in causal survival analysis’ \nAbstract: Several methods are available for estimating the causal effect of time-varying treatment strategies on survival outcomes in observational studies. These include singly robust methods such as inverse probability weighting (IPW) that requires a sequence of correctly specified models of the observed treatment distribution (the propensity score)\, and iterative conditional expectation (ICE) that require a sequence of correctly specified models of the nested conditional outcome means. Alternatively\, doubly robust estimators that combine IPW and ICE require that only one of the sequences of models be correctly specified\, and thus offer more than one opportunity for valid estimation. In recent years\, these methods have been generalized to accommodate effects of stochastic strategies such that treatment assignment at each time is non-deterministic within levels of the measured past. Many authors have considered stochastic strategies that depend on the propensity score which would suggest that doubly robust estimators are not possible to construct. However\, this is not the case. In this talk\, I will give an intuition into why some strategies that depend on the propensity score can still be estimated by doubly robust estimators\, and describe a class of stochastic treatment interventions that will always have doubly robust estimators in point treatment processes and multiply robust estimators in longitudinal observational studies. I also propose a new stochastic treatment intervention dependent on the propensity score motivated by an application to Pre-Exposure Prophylaxis (PrEP) initiation studies that allows doubly and multiply robust estimators. \n\nTo find out how to attend the seminar via MS Teams\, please contact alison.quenault@mrc-bsu.cam.ac.uk
URL:https://www.mrc-bsu.cam.ac.uk/event/bsu-seminar-stochastic-treatment-interventions-in-causal-survival-analysis/
LOCATION:Virtual Seminar via MS Teams
CATEGORIES:BSU seminars,Seminar
ORGANIZER;CN="MRC%20Biostatistics%20Unit":MAILTO:research_admin@mrc-bsu.cam.ac.uk
END:VEVENT
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