Elizabeth Williamson

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Professor of Biostatistics and Health Data Science

London School of Hygiene & Tropical Medicine

United Kingdom

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About

Elizabeth Williamson is a Professor of Biostatistics and Health Data Science at the London School of Hygiene & Tropical Medicine. Based in the UK, her research focuses on causal inference, risk prediction modeling, and the effectiveness of health interventions, particularly in the context of COVID-19. Recent publications include studies on mRNA vaccine effectiveness and methodologies for handling biases in observational data.

Recent Grants

Grant: Open

Novel statistical methods to unlock the potential of routinely collected health data: COVID-19 & beyond

Open Date: 2022-03-14

Close Date: 2027-03-13

Articles (16)

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2024

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2021

Collaborators (23)

Jonathan Sterne

University of Bristol

UNITED KINGDOM

Elizabeth Williamson

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About

Recent Grants

Articles (16)

On variance estimation of the inverse probability‐of‐treatment weighting estimator: A tutorial for different types of propensity score weights

Effectiveness of mRNA COVID-19 vaccines as first booster doses in England

Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose?

Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform

Estimating causal effects for binary outcomes using per-decision inverse probability weighting

Cardiorenal effects of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers among people underrepresented in trials: analysis of routinely collected data with emulation of a reference trial (ONTARGET)

Challenges in estimating the effectiveness of COVID-19 vaccination using observational data

Timeliness of childhood vaccination in England: A population-based cohort study

How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial

Propensity score matching after multiple imputation when a confounder has missing data

Multiple imputation approaches for epoch-level accelerometer data in trials

Waning effectiveness of BNT162b2 and ChAdOx1 covid-19 vaccines over six months since second dose

Comparative effectiveness of ChAdOx1 versus BNT162b2 covid-19 vaccines in health and social care workers in England: cohort study using OpenSAFELY

Rates of serious clinical outcomes in survivors of hospitalisation with COVID-19 in England: a descriptive cohort study within the OpenSAFELY platform

Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform

The BRAIN-Q, a tool for assessing self-reported sport-related concussions for epidemiological studies

Collaborators (23)

Jonathan Sterne

Rachel Denholm

Rohini Mathur

Elsie Horne

Aurélien Belot

Helen Curtis

Krishnan Bhaskaran

Henry Potts

Christopher Rentsch

Tianchen Qian

George Hickman

Clémence Leyrat

Alex Walker

James R. Carpenter

Brian MacKenna

Tom Palmer

Lauren Bell

Madeleine Davies

Nigel Arden

Claire Garnett

Helen McDonald

Rochelle Knight

Katherine Fielding

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