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/COLLABORATIVE 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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