PhD in Biostatistics: Design and analysis for longitudinal animal studies with high-dimensional outcomes
Sound experimental design, followed up by rigorous statistical analysis of the results, is of great importance in studies involving animals, where for ethical and economic reasons, we aim to reduce the number of animals used while making sure that the sample size is sufficient to gain the required knowledge. The potential for reducing the number of animals needed by employing efficient experimental designs is huge. When animals or litters are measured repeatedly over time, we can use longitudinal design and analysis methods to gain statistical power. However, existing methods assume that each sample at each time provides only one, or at most a few, measured outcomes (Diggle, Heagerty, Liang and Zeger, 2002).
Meanwhile, modern high-throughput biotechnologies deliver very high-dimensional outcome data from a single sample. While most research acknowledges the dependence amongst different dimensions of the data (e.g. linkage disequilibrium in the genome, spatial correlation in brain function imaging), there is a need for study design and analysis methods that bridge the gap between traditional longitudinal studies and the high-dimensional world of biomedicine.
This project will be concerned with developing a statistical model for the design and analysis of high-dimensional longitudinal studies, based on generalized linear mixed models and Gaussian processes. The student will apply this method to existing mouse functional brain imaging data from the lab of our collaborator Dr Neil Dawson. The aim will be to gain new scientific insights into developmental changes in mouse brain function, and to demonstrate the effectiveness of the high-dimensional longitudinal method for increasing the statistical power and reducing the number of mice needed.
The project will be jointly supervised by Professor Peter Diggle, Dr Frank Dondelinger and Dr James Hensman. The student will be based in the Centre for Health Informatics, Computing and Statistics (CHICAS) in the Lancaster Medical School, Lancaster University. The candidate will also have opportunities to interact with researchers in the School of Mathematics and Statistics and the Lancaster Data Science Institute.
This project would suit a graduate with a 2:1 or 1st class Honours degree or Masters in statistics, machine learning or other numerical field, with an interest in applying statistical approaches to make a positive impact in animal research. The applicant should have some knowledge of either longitudinal data analysis or high-dimensional statistics, and good programming skills in R or Python. Experience in working with brain imaging and molecular data is a bonus, but is not required.
As part of the PhD studentship, the student will receive training in statistical genomics and genetics, longitudinal data analysis, brain imaging technology and neuroimaging statistics, the MCID Imaging software used for mouse brain imaging.
Funding and Eligibility
The studentship is funded for three years by the NC3Rs (https://www.nc3rs.org.uk). Funding covers home fees and a competitive stipend of £18,000/year. It also includes a £22,940 research training and support grant to be used over the three years.
The studentship is open to candidates of any nationality, but NC3Rs will only fund tuition fees at the home level, and will only pay a stipend to UK nationals, or EU nationals who have been resident in the UK for three years prior to application (this can include residence while undertaking undergraduate study).
Students wishing to apply should in the first instance contact Dr Frank Dondelinger (firstname.lastname@example.org) informally to discuss suitability. The closing date for applications is Tuesday 28th February 2017.
PhD Medicine: An anatomical role for adipocytes in normal and pathological bone formation
In light of the growing aged population in the UK and world-wide, the need for therapies to proactively maintain bone health is ever increasing. We aim to investigate if adipocytes, already seen to be producing structures within the bone matrix, are potential candidates to assist with this in depositing bone within the patient’s bone environment. This project would see if adipocytes can be utilised in the long term to proactively address reduced bone quality. There are currently over 10 million people in the UK aged over 65 and over 3 million over the age of 80. The ageing population is at increased risk of a number of bone pathologies including osteoporosis and fractures from falling. Each year there are around twice as many fractures resulting from falls as there are strokes in the over 65, 1 in 3 aged over 65 and 1 in 2 aged over 80 fall at least once/year.
This project will investigate if adipocytes within the bone environment are capable of producing proteins that contribute to bone matrix and in the longer term offer the opportunity to investigate whether this may generate an additional means of treatment without the need for pharmacological agents targeting bone cells for individuals at fracture risk. Techniques to be utilised include primary cell culture, PCR, flow cytometry, histology and immunohistochemistry. The candidate will join a focused research group with the ability to work across a number of disciplines and a strong publication record. The project will collaborate with departmental colleagues and local NHS clinicians including Orthopaedic Surgeons and Rheumatologists.
The candidate will demonstrate the ability to work independently on the project and prior experience of any of the above skills is desirable.
This post also offers a number of hours of anatomy teaching. Individuals who have an interest in a career in anatomy are particularly encouraged to apply.
Appointment to the position is dependent upon possession of an upper second class honours degree, or greater, in a science based subject, or to be on course for at least an upper second class honours degree at time of application.
Students wishing to apply should in the first instance contact Dr Adam Taylor (email@example.com) informally to discuss suitability. The closing date for applications is Monday 3 April, 2017.