MRC BIOSTATISTICS UNIT

				CAMBRIDGE

			2 MRC funded PhD studentships

The MRC Biostatistics Unit has 2 PhD studentships available from October 2000, 
for a period of three years. The Unit is situated in Cambridge and is a leading 
centre for research in statistics with applications to the biological sciences. 
Main interests of the Unit are Markov chain Monte Carlo methodology and software 
development, genetic epidemiology, cancer screening, cancer epidemiology, AIDS 
projections, BSE, mental health and ageing, performance indicators, 
transplantation, non-standard aspects of clinical trials and meta-analysis. 
Applicants should have a good first degree in mathematics or a closely related 
subject, and preferably an MSc or Diploma.

Applications by post or e-mail, including a copy of the full CV and contact 
details of two referees should  be sent to Dr. Linda Sharples, MRC Biostatistics 
Unit, Robinson Way, Cambridge, CB2 2SR.

Proposed PhD projects.

Performance indicators

In recent years there has been considerable interest in clinical effectiveness 
and the use of indicators to identify centres or individuals whose performance 
is, by some definition, discordant. Examination results have been used for some 
time to tabulate the performance of schools although there has been some 
resistance to publishing 'value-added' comparisons. There are many parallels to 
the medical context: case-mix adjustment is considered of fundamental 
importance, although outcomes are generally events rather than continuous 
measures, and there is a similar danger of naive creation of 'league tables'.

Among the issues to be addressed in this project are: 
1. Strategies for case-mix adjustment
2. Relative merits of survival methods compared with logistic regression
3. Impact of assumptions regarding missing data
4. Detecting outliers in multi-level data
5. Assessment of trends across time
6. Accommodation of ceiling effects

The student will be directly supervised by Dr. Linda Sharples, with Dr. David 
Spiegelhalter and Dr. Sheila Gore acting as close advisors. In addition, close 
collaborative links with the Royal College of Surgeons and UK Transplant Support 
Services Authority are in place. Access to some national audits in 
transplantation and surgery will be available for use as case studies.

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Methods for missing outcome data: application to large scale trials in 
psychiatry

Outcome data for all the subjects participating in a randomised trial are not 
always available.  For 
example, outcomes in psychiatry trials often depend on assessments or 
questionnaires of the patients 
involved, and will be missing for those patients who do not attend or respond at 
follow-up.  Missing 
outcome data prevent intention-to-treat analysis, and cause bias because those 
subjects with missing 
data are not representative of all those in the trial.  Missing data can 
therefore create substantial 
interpretational problems for trials in this area.

Issues to be addressed in this project include:
1. Strategies for describing the pattern, or determining the mechanism, of 
missing data
2. The assumptions made by methods used in practice
3. How available baseline, interim, or other concurrent outcome data can assist 
analysis
4. Use of ancillary data (such as reasons for missingness)
5. Bayesian modelling approaches
6. Strategies for the use of sensitivity analyses in practice
7. Designs which can help mitigate the problems caused by missing data

The project will be based initially on two large scale pragmatic randomised 
trials.  A recently 
completed UK trial of 700 psychotic patients investigated the effectiveness of 
intensive case 
management, and has substantial data at baseline, 1 and 2 years.  Questionnaire 
assessment scores were 
missing in around 20% of subjects at 2 years.  The second trial, which will be 
completed in February 
2001, is evaluating cognitive behavioural therapy for the prevention of 
parasuicide (deliberate 
self-harm) in at-risk patients.  Extensive psychometric and health care cost 
data are available at 
baseline, 6 and 12 months.

The project will be supervised by Professor Simon Thompson, with Dr Wally Gilks 
and Dr Tony Johnson 
acting as advisors.

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