IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
DIVISION OF PRIMARY CARE AND POPULATION HEALTH SCIENCES
RESEARCH STUDENTSHIPS AND COLLABORATIVE RESEARCH STUDENTSHIPS
IN EPIDEMIOLOGY, BIOSTATISTICS AND SOCIAL STATISTICS
We are seeking five enthusiastic and highly motivated individuals to join a
prestigious and stimulating research environment in the Department of
Epidemiology & Public Health, which currently has nine PhD students and
research grants totalling ca £5.3 million.
The Department's focus includes leading-edge work in epidemiology, spatial
statistics and Bayesian statistical methods, medical statistics, statistical
computing, clinical trials and meta-analysis. There is considerable
potential to add to the list of possible projects below, and candidates are
encouraged to submit proposals relevant to the Department's work in
discussion with an appropriate supervisor. Four of the available
studentships, including a collaborative research studentship, are funded by
the
Medical Research Council (MRC), and one collaborative research studentship
is funded jointly by the Economic & Social Research Council (ESRC) and
Ealing Hammersmith & Hounslow Health Authority.
ELIGIBILITY:
PLEASE CHECK YOUR ELIGIBILITY for these studentships by reading the
eligibility rules which may be accessed by clicking on Research Council
Studentships eligibility information at:
http://www.ic.ac.uk/templates/prospectus_text_3.asp?P=1397
Candidates for Research Studentships who are in any doubt about their
eligibility (eg: those with overseas connections, concerned about residence
qualifications) should submit IN WRITING the following details to: Higher
Degrees Assistant (Postgraduate Medicine), Imperial College School of
Medicine, Commonwealth Building, Hammersmith Hospital, Du Cane Road, London
W12 0NN, Fax: +44 (0)208 743 6764. DETAILS: (1) Date of Birth, (2) Place of
birth, (3) Dates and places of residence (self and parents), (4) Academic
qualifications with dates and places, (5) Details of any employment since
graduation, (6) Source of first degree funding.
APPLICATIONS:
Please send a full CV, including details of referees and a covering letter
explaining your research interests, to Simon Sheffield at:
[log in to unmask] , or by fax to: +44 (0)207 402 2150, or by post to:
Simon Sheffield, Departmental Manager, Department of Epidemiology and Public
Health, Imperial College School of Medicine, Norfolk Place, London W2 1PG,
UK.
POSSIBLE PhD PROJECTS, TO COMMENCE 1st OCTOBER 2000
1. Quantifying and explaining variations in health in relation to
biological and social risk factors at small-area scale (ESRC Collaborative
Research Studentship). For informal discussion, contact Dr Nicky Best
(phone: +44 0207 594 3320; email: n.best @ic.ac.uk)
2. Determination of the relationship between maternal haemaglobin, mean
corpuscular volume and birthweight and differentiation of anaemia from
plasma volume expansion. For informal discussion contact Dr Tina Kold-Jensen
(phone: +44 0207 594 3335, email: [log in to unmask])
3. Studies of exposure and health effects near landfill sites - a
number of projects are possible in this area and candidates should contact
Dr Lars Jarup for informal discussion (email: [log in to unmask] ; phone: +44
0207 594 3337).
4. Effect of measurement error in dietary and nutrient variables in the
International Population Study of Macro- and Micro-nutrients and Blood
Pressure (INTERMAP) For informal discussion, contact Professor Paul Elliott
(phone: +44 0207 594 3328, email: [log in to unmask])
5. Dietary and nutritional factors in relation to blood pressure: the
INTERMAP study. For informal discussion, contact Professor Paul Elliott
(phone: +44 0207 594 3328, email: [log in to unmask])
6. Birth and placenta weight as measures of childhood and adult health;
a follow-up of 40,000 children born from 1972 = 1987 in an area of Denmark.
For informal discussion, contact Dr Tina Kold-Jensen (phone: +44 0207 594
3335, email: [log in to unmask])
7. Investigations on the influence on birthweight of maternal
socioeconomic status, maternal build and response to pregnancy; a study of
400,000 births in the Thames area during a ten-year period. For informal
discussion, contact Dr Tina Kold-Jensen (phone: +44 0207 594 3335, email:
[log in to unmask])
8. Space-time modelling of infectious disease surveillance data. For
informal discussion, contact Dr Leonhard Knorr-Held (email
([log in to unmask] )
9. Heterogeneity in couple fertility: the use of frailty models. For
informal discussion, contact Dr Mike Joffe (phone: +44 0207 594 3338, email:
[log in to unmask])
10. Lifetime biological and social risk and protective factors in
prediction of adult health behaviour. For informal discussion contact Dr
Marjo-Riitta Jarvelin (phone: +44 0207 594 3345, email: [log in to unmask])
11. Early childhood health as a predictor of adult health. For informal
discussion contact Dr Marjo-Riitta Jarvelin (phone: +44 0207 594 3345,
email: [log in to unmask])
12. Comparison of methods for analysing repeated measurement data and
problems due to the presence of missing observations. For informal
discussion, contact Dr Rumana Omar (phone: +44 0208 383 1768, email:
[log in to unmask])
13. Evaluation methods of genome mapping. For informal discussion, contact
Dr Berthold Lausen (phone: +44 0208 393 3255, email:
[log in to unmask])
14. Impact of dosimetric and other uncertainties on estimation of cancer
risks from the Japanese atomic bomb survivors. For informal discussion,
contact Dr Mark Little (phone +44 (0)207 594 3379, email - temporarily c/o:
[log in to unmask])
PROJECT DESCRIPTIONS
1. Quantifying and explaining variations in health in relation to
biological and social risk factors at small-area scale (ESRC Collaborative
Research Studentship). For informal discussion, contact Dr Nicky Best
(phone: +44 0207 594 3320; email: n.best @ic.ac.uk)
It is well established that socio-economic disadvantage has a powerful
impact on morbidity and mortality. This project aims to improve our
understanding of the reasons underlying these social inequalities in
ill-health. The aim will be to develop models to describe the risk profiles
of affluent and deprived areas in terms of biological and social
characteristics, plus measures of environmental pollution, measured at
individual and area level. These will then be combined with small-area
models of disease outcomes to attempt to separate and quantify the different
sources of variation in disease risk.
Health outcomes of interest may include various cancers, particularly
breast cancer; hospital admissions for e.g. respiratory illness; and low
birthweight. Data on potential risk factors are available from various
sources, such as the Health Survey for England, the 1991 UK Census, and
national pollution datasets. Other sources of information on e.g. primary
care, income, and crime, will be explored in collaboration with Ealing,
Hammersmith and Hounslow Health Authority who are the industrial partners
for this project.
The findings from this project will be fed directly into Local Authority
Health Strategies, including Regeneration Strategies and Community Plans, to
reduce social exclusion.
2. Determination of the relationship between maternal haemaglobin, mean
corpuscular volume and birthweight and differentiation of anaemia from
plasma volume expansion. For an informal discussion contact Dr Tina
Kold-Jensen (phone: +44 (0)207 594 3335, email: [log in to unmask])
Objectives: To determine the relationship between maternal haemoglobin
(Hb), mean corpuscular volume (mcv) and birthweight and differentiate
anaemia from plasma volume expansion.
Background: Pregnancy produces plasma volume expansion and haemoglobin
concentration falls accordingly. Mcv however does not change substantially.
A drop in Hb with a large fall in mcv causes anaemia, whereas a drop in Hb
with little or no fall in mcv is caused by plasma volume expansion. Severe
anaemia (<80g/l) is associated with the birth of small babies (from both
pre-term labour and growth restriction) but so is high Hb (>120 g/l). The
incidence of low birthweight and of pre-term labour is at its lowest when
the haemoglobin concentration is between 95 and 105 g/l but to date there
have been no studies that have taken into account changes in mcv.
Design/Approaches: The majority of obstetric units in the North West Thames
region have used St Mary’s Maternity Information System (SMMIS) since 1988
to record clinical information throughout pregnancy and delivery. This
database contains information on birth weight and gestational age and now
comprises more than 400,000 pregnancies and births. Full blood counts are
recorded on computer at the pathological departments and download from these
systems will be obtained and linked to the SMMIS data set.
Training: The Ph.D. student will gain experience in analysing large
datasets with repeated events (some women have many blood samples drawn) and
modelling variables. Also the student will obtain the downloads and link the
two databases.
3. Studies of exposure and health effects near landfill sites. For
details and informal discussion, contact Dr Lars Jarup (email:
[log in to unmask] ; phone: +44 0207 594 3337).
4. Effect of measurement error in dietary and nutrient variables in the
International Population Study of Macro- and Micro-nutrients and Blood
Pressure (INTERMAP) For informal discussion, contact Professor Paul Elliott
(phone: +44 0207 594 3328, email: [log in to unmask])
Measurement error is a key issue in many epidemiological studies. Bayesian
modelling offers a flexible way of realistically modelling a large variety
of measurement error situations, of
combining sources of information on the measurement error process, and of
coherently propagating uncertainty of the parameter estimates.
Methodological issues connected with correlated measurement errors on
several covariates and their consequences, with model choice and model
misspecification for the distribution of the unobservable covariates, are
topics to be investigated. Applications will inlcude the INTERMAP study of
diet and blood pressure (see below). The project will involve implementation
of MCMC methods and some computer programming.
The INTERMAP study is an international co-operative epidemiological
investigation into the relation of dietary macronutrients, micronutrients
and other factors with blood pressure. The study comprises more than 4,700
men and women aged 40-59 in 17 population samples in four countries (UK,US,
China, Japan). Data collection included four 24-hour dietary recalls, two
24-hour unrine collections (for the measurement of urinary excretion of
sodium, potassium, calcium, magnesium and urea -- as a marker of protein
intake),extensive information on alcohol intake, questionairre data, and
repeated measures of height, weight and blood pressure (8 times in total on
four occasions). There was standardised training and extensive quality
control in all aspects of the study. The dietary information has been
converted into nutrients following, as necessary, extensive updating of the
existing nutrient databases for the participating countries. The study
database is currently being finalised and the first publications from the
study are due to be completed this year.
The study offers an ideal opportunity to investigate the extent and effect
of measurement error in dietary, nutrient and other variables based on a
highly standardised and well characterised nutrient dataset, and will give
an excellent opportunity to apply latest hierarchical statistical methods to
investigate this problem.
5. Dietary and nutritional factors in relation to blood pressure: the
INTERMAP study.
For informal discussion, contact Professor Paul Elliott (phone: +44 0207 594
3328, email: [log in to unmask])
The study offers an excellent opportunity to investigate the associations of
micro and macro-nutrient intake with blood pressure within the context of a
large epidemiological study, with excellent data on both blood pressure and
dietary exposures. A range of potential projects are available depending on
the experience and interests of the candidate.
The INTERMAP study is an international co-operative epidemiological
investigation into the relation of dietary macronutrients, micronutrients
and other factors with blood pressure. The study comprises more than 4,700
men and women aged 40-59 in 17 population samples in four countries (UK,US,
China, Japan). Data collection included four 24-hour dietary recalls, two
24-hour unrine collections (for the measurement of urinary excretion of
sodium, potassium, calcium, magnesium and urea -- as a marker of protein
intake),extensive information on alcohol intake, questionairre data, and
repeated measures of height, weight and blood pressure (8 times in total on
four occasions). There was standardised training and extensive quality
control in all aspects of the study. The dietary information has been
converted into nutrients following, as necessary, extensive updating of the
existing nutrient databases for the participating countries. The study
database is currently being finalised and the first publications from the
study are due to be completed this year.
6. Birth and placenta weight as measures of childhood and adult health;
a follow-up of 40,000 children born from 1972 = 1987 in an area of Denmark.
For informal discussion, contact Dr Tina Kold-Jensen (phone: +44 0207 594
3335, email: [log in to unmask])
At the Department of Gynaecology and Obstetrics at Odense University
Hospital in Denmark all pregnant women who attended prophylactic antenatal
care- between 1972 and 1987 (N=40,666) were routinely interviewed by a
secretary. Some women delivered more than once during the study period
(28,629 different women). At the first antenatal visit at the 20th weeks’ of
gestation, reproductive, medical, educational, occupational, and lifestyle
histories were recorded by a secretary. At delivery a midwife recorded birth
weight and length, placenta weight, diameter and thickness, funniculus
length and number of vessels. The children are now between 13 and 27 years
old and it is possible to link them to Danish central register of all
hospital submissions (Discharge diagnosis), The Cancer Register and the
Death Register. It is also possible to look at siblings with same
intrauterine environment.
7. Investigations on the influence on birthweight of maternal
socioeconomic status, maternal build and response to pregnancy; a study of
400,000 births in the Thames area during a ten-year period. For informal
discussion, contact Dr Tina Kold-Jensen (phone: +44 0207 594 3335, email:
[log in to unmask])
The purpose of this study is, by the use of almost half a million births, to
investigate the influence on birthweight of maternal socio-economic status
(Carstairs index), maternal build (height, weight, body mass index), and
response to pregnancy (as assessed by the fall in haemoglobin concentration,
changes in blood pressure and pre-eclampsia during pregnancy). These
variables are, however, not operating independently and therefore a model
taking into account the combined effect of these factors will be developed.
This will make it possible to predict the birthweight of a baby based on the
maternal characteristics and may give rice to useful recommendations about
ideal body build, blood pressure and haemoglobin levels for successful
pregnancy.
8. Space-time modelling of infectious disease surveillance data. For an
informal discussion, contact Dr Leonhard Knorr-Held (email
([log in to unmask] )
This goal of this project is to develop statistical methodology for the
analysis of infectious disease data in space and time. Such data typically
arises as so-called surveillance data. Classical methods for infectious
disease incidence mostly analyse small groups such as households, and focus
on estimating transmission parameters of the underlying epidemic model. A
different aim is to describe and predict spatio-temporal patterns of
diseases over a large region.
Recent approaches for time-space variation of non-infectious diseases will
be modified to properly describe and predict outbreaks and decline of
various infectious diseases such as Influenza, Measles, Mumps or Meningitis.
Space-time interactions as well as seasonal patterns are inherent features
of such data and have to be incorporated in the model. Furthermore,
adjustments have to be made to appreciate the fact that individuals, already
being infected, become immune and are no longer susceptible to the disease.
Typically the models will have to incorporate the numbers of infected cases
at time t-1 as additional covariates with possibly non-linear effects on the
rate of infection at time t. For each area, this might also involve the
cases in neighboring areas with different weight structures to be
considered. Finally, there is need to account for sampling variability and
additional unstructured heterogeneity. Throughout, Markov chain Monte Carlo
methods will be used as the current state-of-the-art inference technique for
such complex
models.
9. Heterogeneity in couple fertility: the use of frailty models. For
informal discussion, contact Dr Mike Joffe (phone +44 0207 594 3338, email:
[log in to unmask])
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.
10. Lifetime biological and social risk and protective factors in
prediction of adult health behaviour. For informal discussion, contact Dr
Marjo-Riitta Jarvelin (phone: +44 0207 594 3345, email: [log in to unmask])
Aims are:
To explore the effects of childhood social standing considering biological
modifying factors, and own health behaviour in adolescence, on nutritional
factors (e.g. obesity), physical fitness, smoking and drinking up to age
30 years and to study variations by populations/population groups.
To explain the association of health behaviour and lifetime biological and
social risk and protective factors with the self-assessed health, health
related quality of life and with inequalities in health until the age of
thirty, by population groups (gender, social class, marital, employment
status, country). We will use the opportunity offered by social differences
between countries to compare influence of social factors on health and
health behaviour.
Populations and data:
The study consist of the two national datasets, one from Finland and one
from Britain, representing the sme generation and have similar ages of
follow-up (pregnancy, birth, at 1 or 2 years , teenage years 13-16 years and
31-33 years). The Finnish study (Cohort 1966) consists of 12231 births in
1966. Data collection started in the 24th gestational week on social
background, health and pregnancy and delivery and in the British Cohort for
(n=17733) data has been collected from medical records, by questionnaires
and interviews. In the questionnaires of 31 years follow-up in 1997-8 for
Cohort 1966 the comparability with existing British database was taken into
account. Data has also been collected from various registers and the
existing datafiles comprise thousands of variables. Outcomes at age 30:
nutritional status (weight, height, waist-hip measurement, body mass index),
drinking, smoking, physical fitness and self-assessed health, health related
quality of life measure, social status at 30 (education, income, household
facilities, marital status, employment). Explanatory variables at various
ages: maternal smoking, parental education and social standing at birth and
at 14, parental health behaviour at subject’s age of 14, subject’s smoking
and drinking at teenage and body size. Subject’s health at birth, at 1 and
14. We will apply the cumulative social class measure that takes into
account the class of the parents and the subject’s occupational history, to
present occupational (social) class in predicting the differences in current
health and health behaviour.
11. Early childhood health as a predictor of adult health. For informal
discussion, contact Dr Marjo-Riitta Jarvelin (phone: +44 0207 594 3345,
email: [log in to unmask])
Aim:
To study the association between early childhood health and well-being and
adult health (/adolescent health). The key question is that are the people
in lower socioeconomic groups less healthy than the people in higher
socioeconomic groups because they experienced more health problems in
childhood? What is the contribution of health selelction?
Populations and data:
The study consist of the two datasets, from northern Finland, one for 1966
(n=12231) and one for 1985-86 (n=9479). Data collection started in the 24th
gestational week on social background, health and pregnancy and delivery.
Data has been later collected from medical records, by questionnaires,
interviews and clinical examinations. Data has also been collected from
various registers and the existing data files comprise thousands of
variables. Outcomes at age 30: self-rated health, both hospital and
non-hospital treated diseases. Explanatory and confounding variables at
various ages: health variables since birth (to some extent prenatally) and
early variables (metal disorders, neurological disabilities, other long term
diseases, living conditions, health behaviour.
12. Comparison of methods for analysing repeated measurement data and
problems due to the presence of missing observations. For informal
discussion, contact Dr Rumana Omar (phone: +44 0208 393 3255, email:
[log in to unmask])
A variety of methods are available for the analysis of repeated measurement
data such as the use of summary statistics, hierarchical random effects
models and marginal models. We have recently compared various methods for
repeated measurement analysis for the case of continuous outcomes from a
clinical trial. Some methods are relatively simple to use, but the other
more complex methods have greater flexibility. Classical multilevel models
impose more restrictive distributional assumptions compared with Bayesian
and marginal models. Issues in statistical analysis tend to be more
complicated for discrete outcome data. Furthermore, observations may be
missing at intermittent times and the presence of missing observations can
make the analysis more problematic. If in particular the reason for the
missingness of an observation is related to the outcome variable being
investigated, it complicates both the analysis and the interpretation of the
data. The various methods available for the analysis of repeated measurement
data differ in their sensitivity to missing data. Some methods require
‘missing completely at random’ (MCAR) assumption, whereas others require
‘missing at random assumption’ (MAR). In practice it is possible that data
are not missing at random (NMAR).
This project has two primary objectives. It will examine how the various
methods available for the analysis of repeated measurement data compare in
terms of flexibility, ease of application, interpretation and underlying
assumptions for both continuous and discrete outcome data. A number of data
sets both from clinical trials and longitudinal observational studies will
be used for this purpose. The project will also explore methods for
investigating the patterns of missingness in the data, that is whether the
missing data mechanism is MAR, MCAR or NMAR, and propose strategies to deal
with missingness in the analysis.
13. Evaluation methods of genome mapping. For informal discussion, contact
Dr Berthold Lausen (phone: +44 0208 393 3255, email:
[log in to unmask])
The construction of a genetic or a physical map is a basic goal of all
genome projects. The underlying mathematical/computational challenge can be
seen as a variant of the travelling salesman problem. The aim is to find the
order (of visits) of the genetic marker loci which minimises the sum of the
distances. Consequently, optimal solutions are not feasible for relative
large numbers marker loci.
The PhD-project aims to improve the statistical theory and to address
several important data analytic issues. The project will be collaborative
with Tim Aitman of the Molecular Medicine Group, MRC Clinical Sciences
Centre, Hammersmith campus). It will focus on radiation hybrid (RH) mapping.
One RH example is the data set of the Insulin Resistance Team (headed by Dr
Tim Aitman) (Al-Majali, K.M., et al., 1999, A high-resolution radiation
hybrid map of the proximal region of rat chromosome 4, Mammalian Genome 10,
471-476).
For example important data analytic issues of ongoing research projects are
to model the measurement error caused by possible PCR failure or weak
positive results or to estimate and model jointly linkage groups of the RH
data and information of genetic maps. The development of evaluation methods
of constructed and published genome maps is an important issue of genome and
post genome projects. Recently bootstrap methods are suggested to provide a
measure of stability. The PhD-project will address the data analytic issues
and will develop and analyse bootstrap evaluation by means of Monte-Carlo
simulation and mathematical considerations.
The project is an excellent opportunity to collaborate in an important and
fast developing research area. Moreover, the project provides research
experience in highly important application fields of statistical genetics
and bioinformatics.
14. Impact of dosimetric and other uncertainties on estimation of cancer
risks from the Japanese atomic bomb survivors. For informal discussion,
contact Dr Mark Little (phone +44 (0)207 594 3379, email - temporarily c/o:
[log in to unmask])
Objectives: Development of improved methods for analysis of random and
systematic dosimetric uncertainties in the Japanese atomic bomb survivor
Life Span Study (LSS) data, and consideration of uncertainties in projecting
radiation-induced cancer risks to low doses, to the end of life, and across
populations to enable a full assessment to be made of uncertainties in low
dose-rate cancer risk.
Methods and data: Bayesian Markov Chain Monte Carlo models will be fitted to
the latest LSS cancer incidence and mortality data. Various dose measurement
error models will be investigated. No standard statistical software can be
used for implementing these algorithms and hence special computer programs
will be written. A variety of complex disease models (hybrid
absolute/relative risk, mechanistic multistage) will be investigated, and in
particular a variety of different dose-responses (linear, quadratic,
exponential) will be assessed. There will be interactions with researchers
in the University of Munich, INSERM U.170 (Paris), the National Radiological
Protection Board and RIVM (Bilthoven) as part of a collaborative EC
contract.
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