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    MRC Biostatistics Unit PhD Studentships (University of Cambridge,
    United Kingdom) - January 2012 entry

The MRC Biostatistics Unit (BSU) is an internationally recognised 
research unit specialising in statistical modelling with application to 
medical, biological or public health sciences. Details of the work 
carried out in the Unit appear on our website. See 
http://www.mrc-bsu.cam.ac.uk/

The BSU is offering 2 MRC Studentships (UK or EU national) to commence 
in January 2012:

- Full awards cover Cambridge University fees (at the Home/EU rate only) 
and a stipend of at least 13,590 pounds for a period of 3 years only.
- Partial awards only cover Cambridge University fees (at the Home/EU 
rate only).

Eligibility criteria apply, for details see the Student qualifying and 
eligibility requirements section on our website.  See 
http://www.mrc-bsu.cam.ac.uk/Education/PhD.html

Applicants must have or expect to get a first or high 2.1 honours degree 
in mathematics, statistics or a related discipline. A masters degree is 
essential. The list of the projects available for PhD study are given 
below.

All prospective students wishing to apply to the BSU PhD programme must 
enter a preliminary competition before completing the University's 
GRADSAF (except for applicants who are not eligible for MRC funding and 
need to apply for Scholarships from the University of Cambridge).

***_PhD Studentship- January 2012 entry_*:
*
Full PhD Studentship (UK /EU National)*
- 3 years PhD position for UK or EU national
- Available for January 2012 Entrance
- Fully funded for Fees and Maintenance at standard MRC (Home Student) 
rates.

*Partial PhD Studentship (UK /EU National)*
- 3 years PhD position for UK or EU national
- Available for January 2012 Entrance
- Fees only funded at standard MRC (Home Student) rates.

Partial awards cover Cambridge University tuition fees (at the Home/EU 
rate only), but not maintenance stipend. This is also called a Fees-only 
Studentship. Partial funding is available for UK nationals and citizens 
of EU countries only.

*Application deadlines
*Application deadline: Friday, 28th October 2011.
These PhD Studentships are expected to commence at the start of the Lent 
term in January 2012.
Students wishing to apply for funding from the University should check 
the University's website for details of the closing date for these 
(early December).*

How to apply
*
All applicants should send:
- CV
- Cover letter
- Detailed list of all statistics courses taken
- Contact details of 2 academic referees to the Postgraduate 
Administrator at the Biostatistics Unit.
E-mail applications are acceptable to [log in to unmask]

*Projects**
1. Multi-state survival modelling with time-dependent covariates: 
estimation and prediction - Supervisors: Ardo van den Hout and Fiona 
Matthews
*Multi-state survival models are used in medical research to investigate 
and predict health-related processes over time. Applications are, for 
instance, stages of recovery after an operation, stages in the 
development of AIDS, and stages of disability in older age. In a 
continuous-time multi-state model, the risk of a transition to a next 
stage can be linked to covariates (risk factors) such as age, sex, and 
smoking behaviour.

In a three-state model for disability in older age (healthy state, 
disability state, and death state), a typical research question is 
"Which risk factors play a role with regard to a transition from the 
healthy state to the disability state?". A question that has to do with 
prediction is "Given no disability at age 65, what is the total life 
expectancy, and which part of that is expected to be disability-free?".

Statistical methodology for multi-state models is available and can be 
seen as an extension of standard survival analysis. An often used 
assumption is that the multi-state stochastic process is first-order 
Markovian. Such an assumption makes the models relatively easy to 
estimate. Extensions to Semi-Markov models can also be found in the 
literature. When estimating the model, it is possible to take into 
account covariates that change over time. However, if the change of such 
a time-dependent covariate is a stochastic process (for example, 
changing smoking behaviour), then the prediction of the multi-state 
process is not straightforward. The multi-state process is dependent on 
the stochastic process of the covariate and joint modelling of the 
processes is required.

The project will start with methodology for the estimation of 
continuous-time multi-state models. Time-dependent covariates will be 
taken into account and the parallels with survival analysis will be 
explored. Next, prediction and the joint modelling will be undertaken. 
Possible extensions of the methodology may include semi-Markov models 
and/or multi-level models that take unobserved heterogeneity into 
account. The models will be applied to data from the Cognitive Function 
and Ageing Studies to predict changes associated with ageing.

Population ageing is of growing interest, and this work contributes by 
improving methodology for predictions for the future.

*Collaborators: *Cognitive Function and Ageing Studies

*2. Longitudinal modelling of HIV markers to estimate time of infection 
and HIV incidence in the general population - Supervisors: Brian Tom and 
Daniela de Angelis
*In the late 1990's the idea of identifying new HIV infections using 
characteristics of the antibody response following infection was 
introduced. Since then a number of antibody biomarkers have been 
developed to distinguish between recent and established HIV infection. 
Typically a specific threshold/cut-off of the biomarker is chosen, 
values below which are indicative of recent infections.

Such biomarkers have attracted considerable interest as the basis for 
incidence estimation using a cross-sectional sample. An estimate of HIV 
incidence can be obtained from the prevalence of recent infection, as 
measured in the sample, and knowledge of the time spent in the recent 
infection state, known as the window period. This idea has been used to 
estimate incidence rates in both counselling and testing sites and in 
sexually transmitted disease clinics.

More recently a biomarker has been proposed based on the principle that 
antibodies produced early after infection bind less strongly to the 
antigen than those produced in established infection. The avidity of the 
antibodies to bind to the antigen can be measured using the Avidity 
Index (AI). Conditionally on the choice of a specific threshold, 
individuals with measured AI below the threshold are classified as 
recently infected and the window period is now the time spent below the 
chosen threshold.

However, a number of challenges presently exist when adopting this 
strategy to HIV incidence. Firstly, the initial estimation method 
suggested by Janssen et al. (1988) assumes that the testing process, 
which leads to an individual presenting for an HIV test, is not 
associated with the risk of infection. Secondly, issues exist regarding 
how estimates of HIV rates derived from a particular sub-population can 
be extended to the more general population of interest. Thirdly, it is 
still unclear how to perform the estimation when only data from 
diagnosed individuals, who have chosen to test, are available.

Research into addressing these challenges is being actively pursued. 
However the current approaches taken currently have been rather 
informal. For this project we propose to tackle this problem of HIV 
incidence using HIV markers in a more rigorous manner. We envisage using 
a Bayesian framework to tackle this problem, whereby longitudinal 
modelling techniques and statistical methods that account for sampling 
biases are employed.

*Collaborators: *Health Protection Agency HIV/STI Department

*For further details please visit our website* 
http://www.mrc-bsu.cam.ac.uk/Education/PhD.html

Many thanks,

Rosemary

---
Rosemary Camperos
PhD Programme Administrator

MRC Biostatistics Unit
Institute of Public Health
University Forvie Site
Robinson Way
Cambridge CB2 0SR

Tel No.: +44 1223 330376
Fax: +44 1223 330388
Email:[log in to unmask]
Website:http://www.mrc-bsu.cam.ac.uk  




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