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The deadline for applications for the post below has been extended to 22 February. Senior Postdoctoral Medical Statistician Medical Research Council Clinical Trials Unit (CTU), London & MRC Centre for Causal Analyses in Translational Epidemiology (CAiTE), School of Social and Community Medicine, University of Bristol Salary £36,000-£40,000 (inclusive of London weighting if in London) An MRC-funded 3-year postdoctoral cross-unit position is available for the following project: Using causal analyses to add value to large Randomised Controlled Trials Supervisors: Dr Deborah Ford, London & Dr Margaret May, Bristol MRC CTU, London is a centre of excellence for clinical trials, meta-analyses and epidemiological studies. The School of Social and Community Medicine, Bristol is one of the UK's leading centres for research and teaching in population health sciences. An excellent opportunity has arisen for a postdoctoral statistician to work in a cross-unit appointment funded by the MRC Population Health Sciences Research Network on a new project to identify how causal methods can be used to maximise value from randomised controlled trials (RCTs). RCTs to address all key questions about patient management are infeasible. By careful application of causal methods it may be possible to address questions in addition to the randomised questions using the data already collected in trials; for example the impact of second-line treatments or concomitant medications prescribed at the clinician’s discretion may be estimated.. The project’s objectives are: (1) to develop “best practice” examples for a primer to guide statisticians working in clinical academia in the use of causal methods (relevant to RCTs and epidemiological studies); (2) to systematically survey the clinical literature, and conduct a scoping exercise at MRC CTU, to identify types of questions which causal models have most potential to answer in RCTs; (3) to apply causal inference methods to use RCT data to answer at least two new clinical questions, in order to provide high-profile examples of their value, as well as potentially refining existing statistical methods; (4) to disseminate best practice through workshops, based on (3), plus previous work in this field. Candidates should have a PhD in Medical Statistics or closely related discipline. Relevant work experience in analysis of clinical trial or observational data would be an advantage. Candidates should have close familiarity with one or more statistical packages (preferably STATA or SAS), an interest in applying and developing new statistical methodologies, have good communication skills and possess initiative. This is a full time position. The appointee would choose to be based in London or Bristol. On average they would spend 1 day/week in the other MRC Unit/Centre (fares covered), consequently they would have the opportunity to work in both units, gaining exposure to two diverse working environments, and with access to experts in causal inference methodology, clinical trialists, epidemiologists and clinicians. This is an MRC band 3 position in London or University grade J position in Bristol. Salary includes London Weighting and Central London Supplement if based in London. Benefits include 30 days annual leave per year (London) or 25 days annual leave plus 7 University closure days (Bristol), a contributory pension scheme and (if based in London) an interest free season ticket loan. Applications are handled by the RCUK Shared Services Centre; for further information and to apply please visit: http://www.topcareer.jobs/Vacancy/irc78529_2651.aspx and click on APPLY. If you are unable to apply online please contact us on 01793 867003 quoting reference number IRC78529. ********************************************************************************* This communication is confidential and may contain privileged information intended solely for the named recipient(s). It may not be used or disclosed except for the purpose for which it has been sent. If you are not the intended recipient, you must not copy, distribute, take any action or reliance on it. 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