To those considering applying for a PhD
This is to let you know that there are further details,
available from Mrs Helen Ludford [log in to unmask] directly
or via
http://www.medschl.cam.ac.uk/gppcru/index.php?option=com_content&view=ar
ticle&id=284:phd-research-studentship&catid=6:vacancies&Itemid=43
The closing date is next Wednesday (23rd July).
Regarding the proposed medical statistics project:
"Understanding how complex interventions do or do not work"
Supervisors
Dr Toby Prevost, General Practice and Primary Care Research Unit,
University of Cambridge
www.medschl.cam.ac.uk/gppcru
Prof Simon Thompson, Director, MRC Biostatistics Unit, Institute of
Public Health, Cambridge
www.mrc-bsu.cam.ac.uk
Lifestyle behaviours are important in preventing, delaying and limiting
the progression of chronic diseases. Changing such behaviours is
challenging to accomplish and typically difficult to evaluate.
Randomised trials often test complex interventions that involve several
potentially impactful "components" (such as: a "theory" that specifies a
linked "chain" of correlated variables that is assumed to apply;
"persons" delivering the intervention; and the organisation of the
content of the intervention to be delivered in "parts" and over "time").
These trials also involve the measurement of "intermediate variables"
that may be causally connected with the behaviour.
This project will be based around the ProActive trial of a complex
intervention to encourage greater physical activity in those at higher
risk of developing Type 2 Diabetes (published in the Lancet in 2008). A
second multiple-behaviour dataset, the Addition plus trial, will be also
be available.
The purpose of the project is to apply and develop statistical methods
to understand how behavioural interventions do or do not work, and to
inform the design of future complex intervention trials in this
important area.
With multiple components, and a chain repeated over time in three trial
arms, there are several areas in which any limitation in intervention
effectiveness could be chosen to be investigated via research questions
such as: Was the assumed underlying theory observed to apply well? To
what extent can the assessment of causal relationships be inhibited by
measurement error? Could the "parts" of the intervention, and their
delivery, inform the potential effectiveness of the intervention? How
could we improve the design of future trials?
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