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We are inviting applications for a PhD studentship in statistical
epidemiology, funded by GlaxoSmithKline for 3 years, starting at a mutually
convenient date. The studentship is open to EU citizens and covers
tuition fees and a generous living allowance.



The project concerns statistical methods in pharmacoepidemiology, using
large primary care data bases, such as the General Practice Research
Database, as described in the project outline below. The project
supervisor is James Carpenter, and the successful candidate will be
collaborating with statisticians and epidemiologists at both LSHTM and GSK
to develop a stimulating doctoral program of research.



Applicants must have an MSc (or equivalent) in statistics. Some knowledge
of epidemiology is desirable but not essential: an aptitude for applied
methodological research in statistics is more important.



Applications, including a CV and the names of two referees, should be sent
to Dr James Carpenter, Medical Statistics Unit, London School of Hygiene
and Tropical Medicine, Keppel Street, London WC1E 7HT (email
<mailto:[log in to unmask]>[log in to unmask]), from whom
further particulars can be obtained. For an informal discussion telephone
James (020 7927 2033) or Professor Stuart Pocock (020 7927 2413). The
closing date for applications is Friday 20 August 2004.



Project outline: Statistical methods in pharmaco-epidemiology using large
general practice databases



This project concerns design issues and statistical methods in
pharmaco-epidemiology, i.e. the study of the use and effects of drug
treatment in populations, both as regards effectiveness and safety.
Although this can be viewed as an application of epidemiologic methods to
pharmaceuticals, the nature of the large routinely collected databases,
which typically form the principal source of information, mean that this
field poses challenges that often require special solutions in both study
design and statistical analysis. Further, the complex and dynamic context
of pharmaco-epidemiology gives rise to fascinating and unique statistical
challenges. Such challenges need to be addressed if the information in such
databases is to be reliably and routinely used.



For instance, for any particular class of drugs (e.g. statins for reducing
risk of coronary disease) it would be appropriate to investigate possible
associations with several other diseases (e.g. Alzheimer's disease, eye
cataract, suicide etc). The case-cohort design seems well suited to such
problems by making use of all disease cases together with a random
sub-sample of the whole cohort. Nested case-control studies for each
disease might be a suitable alternative approach.



The analysis of data from large routinely collected databases also presents
unique challenges. Propensity scores and weighting methods have been
proposed in other settings to reduce bias caused by non-random allocation
of treatments. However, their application to large scale database analyses
poses some special challenges, not least because of the considerable
quantity of missing observations, which are inevitable in a large amount of
routinely collected data. Subjects who have complete data on all exposures
and confounders are likely to be both unrepresentative and a relatively
small proportion of the total. Thus a conventional approach, such as using
only individuals with complete data, is likely to be both biased and
underpowered.



We intend to pursue a program of methodological research to address these
questions. In order to focus and enhance the practical relevance of this
research it will be closely linked to and illustrated by specific potential
drug-disease associations of interest to pharmaco-epidemiologists.



The precise sequence of methodological issues to be tackled will unfold
over time but specific potential topics of interest are as follows:



1. How do case-cohort and traditional case control designs compare, in
terms of efficiency and likely costs?

2. One problem with the case-cohort approach is the difficulty in
taking account of general practice effects since there is no matching of
cases to controls. How much does this apparent deficiency matter?

3. What size of sub-cohort should be selected and how one should
restrict its sampling frame to take account of the characteristics of
disease cases?

4. It has been suggested that repeat use of the same sub-cohort for
multiple disease-drug inferences may have some statistical penalty re
non-independence;

5. The use of nested case control studies has the inefficiency of
needing a separate selection of matched controls for each set of disease
cases. However, one can easily match on practice and then the statistical
analysis methods are better established. It will be valuable to compare
results (and the effort to produce them) for nested case-control and
case-cohort design for the same drug-disease questions;

6. Development of methods for coping with missing data in the
analysis, including the use of propensity scores and related approaches
when data are missing. This would involve the exploration and development
of multiple imputation methods that take into account (i) information on
the distribution of exposures and confounders available in the wider
epidemiological literature, and (ii) the hierarchical structure of the data
(with patients nested in practices etc).

7. Given the novel approaches required, the development of appropriate
statistical software and experience in handling such large databases will
be useful for future applications.



Our methodological findings, backed up by experience in real-world
pharmaco-epidemiological questions should have a major impact on how best
to make use of such large databases as the GRPD for studying drug safety
and effectiveness in a primary care setting.



Stuart Pocock
Medical Statistics Unit
London School of Hygiene and Tropical Medicine
Keppel Street
London WC1E 7HT

Tel +44 (0)20 7927 2413 (direct)
                                2230 (secretary)

Fax +44 (0)20 7637 2853