Imperial College School of Medicine

MRC PhD Studentship in Epidemiology and Biostatistics
[CLOSING DATE 17th JULY, 1998]

We are seeking an enthusiastic and highly motivated individual to join a
prestigious and stimulating research environment centred on the Department
of Epidemiology & Public Health. The department's focus includes
leading-edge work in spatial statistics and Bayesian statistical methods,
epidemiology, and statistical computing. 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. Space-time interactions between cardio-respiratory mortality and air
pollution across London. Supervisor: Professor Paul Elliott.

PROJECT DESCRIPTION: A correlation between daily variations in outdoor air
pollution and daily mortality from cardio-respiratory disease has been
consistently reported for different cities in the US and Europe (including
London), although it is unclear to what extent, if any, these findings are
transferable to the effects of chronic exposure to ambient air pollution.
US studies have also reported higher average mortality for cities with
higher average levels of air pollution, but interpretation is complicated
by the broad-scale ecological nature of the data. This project will explore
both temporal and small-area variations in cardio-respiratory mortality
(from the national postcoded mortality dataset held by the Department),
meteorological data, and monitored levels of air pollution across London
(from the South East Institute of Public Health). Data are available for
around 30 monitoring sites: mainly SOx and NOx, but also PM10 at eight
sites. The temporal and spatial components of variability in the health and
the pollution data will be explored and quantified. Possible interactions,
e.g., higher peaks in daily mortality in high compared with low pollution
areas, and the possible effects of areal deprivation measures (calculated
from census statistics) will be explored. Appropriate methods, based on
Bayesian statistics, will be used to analyse and combine the health and
pollution data, making allowance for the correlated nature of the errors in
both space and time.

2.  Heterogeneity in couple fertility: the use of frailty models.
Supervisor: Dr Michael Joffe.

PROJECT DESCRIPTION: The fertility of a variety of populations has been
characterised, using as a measure the time taken by a couple to conceive
(Time To Pregnancy, TTP), and a remarkably large degree of between-couple
heterogeneity has been found. On evolutionary grounds, it is difficult to
explain why a substantial proportion of the population, with no obvious
disease or other health problem, have low per-cycle probability of
conception. Furthermore, the semen quality of men is remarkably poor when
compared with that of other mammalian species. It is unclear whether these
phenomena are linked, and if so, whether they are of recent historical
origin, and/or whether there are major differences between different
populations e.g. according to genetic or nutritional differences. The
possibility of a fall in the sperm count, which appears to have occurred in
certain places (e.g. Paris, Gent), needs to be seen in this context. This
project will explore between- and within-couple fertility in a large
survey, the National Child Development Study, which is representative of
the population born in Britain in 1958. Respondents who were interviewed at
age 33 provided values of TTP for almost all non-accidental pregnancies (91
percent of female and 84 percent of male respondents, N=3132 and 2576,
respectively). These data will be analysed using frailty models, which
introduce a couple-specific element (a random effect) to acknowledge
between-couple differences. The distribution of these random effects may
then depend on characteristics of the couple. The effect of these
characteristics on relative fertility will be explored and quantified, and
implications for models of fertility in the population will be assessed.

3. Modelling the population transmission of gonococcal infection in the
population. Supervisor: Dr Helen Ward.

PROJECT DESCRIPTION: Objectives: To develop a model for the transmission of
gonorrhoea in the population based on analysis of biological and social
data. Methodology: Neisseria gonorrhoeae, the causative organism for
gonococcal infection, can now be typed using various genetic techniques.
This permits fine classification of the organism which can be used to study
transmission of the organism in a population over time, testing and
extending existing epidemiological models for sexually transmitted
infections. Epidemiological analysis of such data require the development
of statistical methods for validating results, and for comparing potential
routes of transmission suggested by different sources of data (e.g. social
compared with genetic). This project would be based first on the detailed
analysis of results from a historical social and microbiological data set,
and then applying statistical and epidemiological techniques to the results
of further microbiological work, including sequence data, in collaboration
with the Department of Microbiology, Imperial College School of Medicine
(led by Dr C Ison). The data set includes social and epidemiological
information and microbiological strains for over 500 cases, including some
large transmission clusters and networks. We intend to investigate the
relationship between epidemiologically-linked individuals and
microbiological strain type. This may be carried out using loglinear
models, taking into account the sampling methods for collecting the data,
and likelihood of incomplete data (including non-disclosure of some sexual
partners and inability to trace some linked individuals). The demographic
make-up of the sexual network which gives rise to the data will also be
analysed using categorical data methods. The aim of the project is to
understand how gonorrhoeae is transmitted thus providing a means of
controlling it's spread.

4. Bayesian methods for modelling variation in health indicators.
Supervisor: Dr Nicola Best.

PROJECT DESCRIPTION: Health ‘indicators’ based on hospital admissions data
vary inherently over space and time. The interpretation of such variation
is complex, and may reflect:  (a) associations between the outcome of
interest and measurable explanatory factors/confounders such as age, sex,
socio-demographic factors and hospital effects; the latter may arise due to
differences in e.g. number of beds, speciality, coding practice, data
quality and completeness. Such relationships may involve non-linear,
possibly discontinuous associations and interactions between the variables.
(b) variation induced by dependence of the outcome on unobserved or
unmeasured factors, such as an unknown environmental pollutant. These
factors will typically vary smoothly in space and time, thus inducing
spatial/temporal correlation in the observed outcome.  (c)  Residual chance
variation. Recent developments in Bayesian computational techniques
(specifically Markov chain Monte Carlo methods) have allowed the realistic
modelling of complex problems. However, there has been limited serious use
of these methods to answer questions of substantive importance. This
project thus aims to develop and extend such Bayesian methods to: a)
realistically model the relationship between health indicators and relevant
measured predictors;  b)  provide flexible models for capturing
spatial/temporal correlation in such data;  c) provide techniques for model
criticism/selection which will enable genuine explanatory associations to
be distinguished from spurious variation arising by chance.The methodology
will be applied to a range of health indicators derived from relevant
post-coded databases held by the department, including the Hospital Episode
Statistics.

5. Errors-in-variables modelling in nutritional epidemiology. Supervisor:
Dr Jon Wakefield.

PROJECT DESCRIPTION: There is strong evidence that diet is an important
factor in the aetiology of many diseases, including cardiovascular disease
and several cancers. However, the study of such relationships is severely
hampered by the difficulty in obtaining reliable measures of dietary intake
in large population samples, and in deriving models of lifetime exposure.
In particular, the large component of within-person variability and
measurement error in dietary and nutritional variables leads to
underestimation of effect (for example, in relation to blood pressure or
coronary heart disease), the so-called 'regression-dilution' problem.While
life-long intake may be the gold standard, in practice data are only
available via food-frequency questionnaires, dietary records, urinary
excretion, etc., and usually on only one occasion.  Each of these sources
of data suffer, to a greater or lesser extent, from problems of measurement
error.  In addition they provide only a snapshot of an individual's diet.
There is large within-individual variability with components due for
example to season, day-of-the-week, and there is also, in general,
correlation between measurements obtained on successive days. When an
outcome such as blood pressure is modelled as a function of dietary
variables it is vital to account for each of these factors, as an analysis
which does not acknowledge the errors-in-variables aspect will produce
biased estimates.  In this project, Bayesian and classical parametric
errors-in-variables models will be utilized to provide corrected estimates
of effect.  Such models contain layers of assumptions.  We will carefully
assess each of these, supported by the dietary expertise within the
departments. A number of large databases, that include repeated measures of
dietary variables, are available for study including INTERSALT and the
Dietary and Nutritional Survey of British Adults. The estimates obtained
from our models will be compared with more-recently proposed
semi-parametric techniques.


INFORMAL ENQUIRIES may be made to Professor Paul Elliott (email:
[log in to unmask], telephone 0171 594 3328), Dr Michael Joffe (email:
[log in to unmask], telephone 0171 594 3338), Dr Helen Ward (email:
[log in to unmask], telephone 0171 594 3303), Dr Jon Wakefield
([log in to unmask], telephone: 0171 594 3336).

APPLICATIONS: please send a letter and CV, including  the names of two
referees, to Mr Simon Sheffield, Departmental Manager, Department of
Epidemiology & Public Health, Imperial College School of Medicine, Norfolk
Place, London W2 1PG (email: [log in to unmask], fax: 0171 402 2150,
telephone: 0171-594 3317).


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%