Fwd: FW: [ISCB] Deadline extended to 16/05/16: 2 x PhD studentships at the Biostatistics Unit, University of Leicester, UK
Tal Galili
From: is...@googlegroups.com [mailto:is...@googlegroups.com] On Behalf Of Bujkiewicz, Sylwia I. (Dr.)
Sent: Friday, April 29, 2016 5:57 PM
To: is...@googlegroups.com
Subject: [ISCB] Deadline extended to 16/05/16: 2 x PhD studentships at the Biostatistics Unit, University of Leicester, UK
2 x PhD studentships are available at the Biostatistics Unit, Department of Health Sciences, University of Leicester, UK
To start: September 2016
Applications close: 16th May 2016 (noon UK time)
Eligibility: UK/EU students with at least a 2.1 degree or equivalent
Studentships
Each studentship will last for 3 years and will cover University Tuition Fees at Home/EU rates, a Student Stipend at Research Council rate (currently £14,057 per annum) and a Research Support Grant (including a travel allowance).
To apply, please follow the instructions here:
Project descriptions can be found below, please contact the supervisors for an informal discussion about the projects.
Project 1: Up-to-date prognostic modelling of more detailed population-based cancer data
Supervisor: Dr Mark Rutherford (mark.ru...@le.ac.uk)
This is an exciting era for cancer epidemiology in the UK with cancer data quantity and quality increasing. More detailed datasets enable researchers to answer new and more comprehensive research questions with the ability to link cancer registry data with HES, CPRD, and more detailed clinical data. The use of this data should improve the understanding of prognosis following a cancer diagnosis with information on diagnostic procedures, tumour characteristics, treatment and clinical outcomes. However, there are many challenges for the appropriate development and application of statistical methods. In particular, more complex analyses makes communication of results more challenging and more thought is needed to ensure results are presented so that key messages are understood by health care professionals, patients and decision makers. A detailed colorectal cancer dataset will be used, with which we can develop and evaluate new methodology based around real-life issues from applied studies. Collaboration with Dr Eva Morris at the University of Leeds will form part of the project. Three specific areas of methodological research to be considered as part of this PhD studentship are; Multiple imputation with time-dependent effects, Model selection in large data sets, and Providing up-to-date survival estimates. This project links to the two supervisors’ main research interests and is directly related to a current CRUK-funded project and the work of other PhD students within the Biostatistics Research Group.
Project 2: Development of statistical methods for the joint modelling of multiple longitudinal biomarkers and time-to-event data derived from linked electronic health records, for use in patient-tailored precision medicine within cardiovascular disease
Supervisor: Dr Michael Crowther (michael....@le.ac.uk)
Clinicians dynamically record changing information in electronic health records, yet most prognostic models consider only a single static value of a biomarker. In cardiovascular disease little is known about how to model both trends over time, and also their association with clinical endpoints . This project will use population based electronic health records linking primary care, hospitalisation registry, heart attack and death registries (the CALIBER programme), to investigate the extent to which current values of biomarkers, such as systolic and diastolic blood pressure, and trends over time, add information beyond a single baseline value in the prediction of death, MI or stroke. This centres on precision medicine, utilising records to tailor predictions at the individual patient level, with the potential to allow targeted interventions to be applied at the optimum time-point, whilst dynamically monitoring patient characteristics as they evolve in “real time”. The project will involve development of statistical methods in the field of joint modelling of longitudinal and survival data, including development of user-friendly software. Possible topics include, 1) Systematic review of joint modelling in cardiovascular disease, 2) Modelling the visiting process: Sicker patients go to the doctor’s more often, 3) Joint modelling of multiple, repeatedly measured biomarkers and their association with survival, 4) Developing model discrimination tools to evaluate multiple predictive biomarkers. Close collaboration will be fostered with Professor Harry Hemingway of the Farr Institute at UCL. The student will have the opportunity to attend and present at national and international conferences.
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