Imperial College School of Medicine

MRC PhD Studentship in Medical Statistics

We are seeking an enthusiastic and highly motivated individual to join a
prestigious and stimulating research environment centred on the Department
of Medical Statistics & Evaluation. The department's focus includes
leading-edge work in  medical statistics, statistical computing, clinical
trials and meta-analysis. Candidates are invited to propose projects in
these areas, in addition to those listed below. Studentships will commence
in October, 1998.

MRC RESEARCH STUDENTSHIP PROJECTS

1. Methods for investigating the relationship between underlying risk and
treatment benefit in meta-analysis. Supervisor: Professor Simon Thompson.

PROJECT DESCRIPTION: The usual assumption in meta-analysis of clinical
trials, that the extent of treatment benefit does not depend  on the
underlying risk of the patients in the different trials, is likely to be
false in many situations.  Indeed the nature of the relationship between
underlying risk and treatment benefit will delineate which patients will
benefit most, and which least, from treatment and will be of crucial
importance in health economic assessments.  Therefore robust methods to
investigate such relationships are needed.  We have recently shown that
naive analyses, which for example use the observed risk of events in the
control group of each trial as a measure of underlying risk, are seriously
flawed for reasons related to regression to the mean.  We have also
developed a method, for treatment effects expressed as odds ratios, which
avoids these biases and can be implemented in the Bayesian software BUGS.
However the method is limited in a number of ways, and its extension is the
purpose of this proposed research.The method needs to be developed to
consider treatment effects on scales other than odds ratios, including
relative risk, absolute risk difference, and number needed to treat (NNT).
Applications to types of outcomes other than binary is also necessary,
including means of continuous data (for example for analysing economic
costs) and censored survival data.  These extensions are not straightforward
because of technical statistical issues, but are crucial for the methods to
become fully useful in both epidemiological and clinical settings.  These
developments will require the use of various software, including BUGS and
the multilevel package MLn, but one aim will be to produce accessible
software that will enable applied researchers to undertake these analyses.
Some very recent publications have also proposed other solutions than our
own.  Comparison between these methods will be undertaken, both empirically
using data from existing meta-analyses and in terms of the different
assumptions of the methods.  Empirical research based on the Cochrane
database of systematic reviews will be carried out to ascertain the extent
to which meta-analyses show currently unrecognised relationships between
underlying risk and treatment effects, and the implications that these have
on the conclusions that should be drawn.  In the cardiovascular field,
available data sets include meta-analyses of antiplatelet therapy,
fibrinolytic therapy, magnesium treatment, and of cholesterol reduction.
Many other data sets are available through the Cochrane Collaboration.

2. Transformation in the analysis of hierarchical medical data, with focus
on fetal monitoring. Supervisor: Professor Patrick Royston.

PROJECT DESCRIPTION: Datasets with a hierarchical or multilevel structure
are increasingly important in medicine. Examples include growth curves,
cluster-randomised trials, multiperiod clinical studies and observational
studies with repeated measures. The multilevel random-effects model is the
analytical tool of choice. When one or more predictors are continuous,
appropriate regression models are needed. Polynomials are almost invariably
chosen, but they are often inadequate. The project will explore the use of
fractional polynomials in multilevel modelling. These involve
transformations of the predictors and offer greater flexibility and
parsimony than ordinary polynomials. Issues such as how to detect and deal
with heterogeneous curve shapes will be explored. Secondly, for continuous
outcome variables, the multilevel model assumes a Gaussian distribution for
relevant parameters. Transformation of the response variable may be needed
to satisfy this condition. However transformation affects all aspects of the
model, including the shapes of the response curves and their heterogeneity
and the distribution of quantities at all levels of the hierarchy. The
project will investigate the effects of response transformation on different
parts of the model. The aim will be to develop techniques which will help
the analyst decide whether and how to transform the response and understand
the effects thereof. A particular application is the analysis of
longitudinal fetal size data to produce `conditional reference intervals',
which are intended to help the clinician detect fetuses whose growth is
faltering. Transformation of predictor and response variables is needed
here. The project will also consider how best to present the predictions
from such models for ease of understanding and use by the clinician. Several
datasets are available to the project.

INFORMAL ENQUIRIES may be made to Professor Simon Thompson, Medical
Statistics & Evaluation (email: <[log in to unmask]> , telephone: 0181
383 1572), and to Professor Patrick Royston, Medical Statistics & Evaluation
(<[log in to unmask]>, telephone: 0181 383 8425).

APPLICATIONS: please send a letter and CV, including  the names of two
referees, to Sandra Griffin, Imperial College School of Medicine, Department
of Medical Statistics & Evaluation, The Commonwealth Building, Hammersmith
Hospital, Du Cane Road, London W12 0NN. EMAIL: <[log in to unmask]>, FAX:
0181 383 8573.






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