Dear all

Please bring the below opportunity to the attention of any interested
students, who can contact me directly for further information.

Best wishes
Matt

Start Date: 1 Jan 2017
Applications Deadline: 30 Nov 2016

"Statistical methods for analysing linked observational data: studying
complications and outcomes in percutaneous coronary intervention"

Supervisors: Evan Kontopantelis, Matt Sperrin and Mamas Mamas
Funding: Standard RCUK stipend (currently £14,296 per year)

Full details/apply:
https://www.findaphd.com/search/ProjectDetails.aspx?PJID=73857


Percutaneous coronary intervention (PCI) is the commonest means of
revascularisation for patients with coronary artery disease in both
the elective and emergency heart attack setting undertaken in the
United Kingdom and worldwide. Every PCI that takes place in the UK is
captured in the British Cardiovascular intervention (BCIS) dataset.
This dataset comprises over 120 variables and 750,000 procedures, so
is a rich resource for building models to address clinical questions
around the treatment of coronary artery disease; for example,
predicting mortality following PCI and how this has changed over time
and what has driven these changes, or assessing optimal treatment
strategies and their outcomes.

Whilst the long-term mortality outcomes following these procedures are
well described, most data around complications is derived from
randomised controlled trials or registries in lower risk patient
cohorts, that do not reflect multi-morbid elderly patients encountered
routinely in clinical practice. Consequently, there is little data
around the frequency and types of complications in such patient groups
and their outcomes following PCI from the national perspective.

This PhD aims to study real world outcomes and complications of
patients undergoing PCI in the United Kingdom using both the British
Cardiovascular intervention (BCIS) dataset and linked primary and
secondary care databases: Clinical Practice Research Datalink (CPRD)
and Hospital Episode Statistics (HES). This will allow, for the first
time, longitudinal assessment of long-term outcomes for patients
undergoing PCI in the primary and secondary care setting. Specific
project aims include: exploring post-discharge complication rates,
their predictors and associated outcomes, and whether these vary by
case-mix, by centre or region, and over time; assessing whether
complications are reported consistently across different data sources
(eg BCIS vs HES) and if not why; exploiting longitudinal complication
and treatment patterns in the hospital and community settings to
assess long-term outcomes and their predictors.

The successful applicant will develop strong computational skills in R
and/or Stata and expertise in advanced statistical methods such as
mixed-effects modelling, multi-state modelling, and multiple
imputation for missing data. There will be opportunities to develop
novel statistical methodologies as required.

The successful applicant will be based within the Division of
Informatics, Imaging and Data Science, at the University of Manchester
(UoM), which hosts the Health e-Research Centre (HeRC) and is part of
the Farr Institute (http://www.farrinstitute.org.uk). The HeRC is an
MRC funded venture across the universities of Manchester, Liverpool,
Lancaster and York, aiming to apply advanced methods to unlock and
harness real-world evidence from health data across Northern England.
The Farr Institute for Health Informatics Research is a UK, MRC funded
institute that aims to deliver high-quality, cutting-edge research
linking electronic health data with other forms of research and
routinely collected data, as well as build capacity in health
informatics research.

The successful applicant will be guided by a strong multidisciplinary
team of cardiologists, biostatisticians and health data scientists
based at the UoM and Keele University, who deliver and publish high
impact clinical and methodological developments and research. In
addition to face-to-face training by the supervisory team, they will
attend relevant local or national training courses to further develop
statistical and computational skills. They will also be able to
benefit from the impressive infrastructure in health informatics
research and education that is well established in the CHI. They will
belong to the Doctoral Training Network (DTN) across HeRC and the Farr
Institute, providing opportunities to further develop transferable and
professional skills with their UK peers to become the future leaders
of the field.
There is a large diversity of careers available on completion of the
PhD including a postdoctoral or lecturer in a health informatics
department or a medical statistician or data scientist in industry and
healthcare sectors.

Applicants should hold (or expect to obtain) a minimum upper-second
honours degree (or equivalent) in a mathematics, statistics or related
subject area. A Masters degree and/or previous research experience
would be beneficial.

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