SOPHIE GREENWOOD

SOPHIE GREENWOOD
SOPHIE GREENWOOD
SOPHIE GREENWOOD

Research PG

About
Email Address
s.greenwood.22@abdn.ac.uk
Office Address
Health Sciences Building
Foresterhill Campus
Foresterhill
AB25 2ZD

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School/Department
School Of Mmsn

Biography

I am a PhD student interested in how we can improve clinical trial methodology, particularly regarding missing data, patient participation and estimands. My thesis explores how patient views can be captured to inform missing data analyses in clinical trials. Prior to starting my thesis I worked as a statistician in industry trials. I also have an MSc in Medical Statistics (2018). 

Qualifications

  • MSc Medical Statistics 
    2018 - University of Leeds 
  • BSc Mathematics and Management (Ind) 
    2017 - University of Leeds 

External Memberships

I am a member of the PSI Communications Committee here. PSI is an organisation of predominantly statisticians who are dedicated to leading and promoting the use of statistics within the healthcare industry for the benefit of patients. 

 

Research

Research Overview

Trial methodology; Expert Elicitation; Missing data; Estimands

Research Areas

Applied Health Sciences

Research Specialisms

  • Medical Statistics

Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is HESA open data, published under the Creative Commons Attribution 4.0 International licence.

Current Research

The decisions statisticis make about "missing data" can directly impact the results of a clinical trial. In my PhD project we are looking to explore if and how patients can be involved in that decision making process using a method called "expert elicitation".

The PoINT project, lead by my lead supervisor Beatriz Goulao, demonstrated that there is interest to have more patient and public involvement in this numerical aspects of clinical trials, along with many others such as target differences.

This type of research is called methodological. It seeks to develop a tool for discussions between participants and researchers about the missing data in trials. The tool aims to provide recommendations to researchers on how to interpret the trial results when some pieces are missing.