You are here

Developing statistical methods to monitor clinical outcomes from large clinical registries

Description 
Clinical registries are health databases of patients with a specific health conditions (e.g. cancer and medical devices), and data from the registries are sometimes used to create benchmarked reports which are fed back to the health system with a view to improve healthcare. Clinical outcomes are also monitored over time to provide an earlier ‘signal’ to indicate if there has been any systematic shift in the process. Current statistical process control methods do not address methodological issues inherent in large observational clinical registries including dealing with selection bias and missing data. This PhD project will focus on developing statistical models or modifying existing models through simulation studies and the application of the models on real-life registry data such as the Australian and New Zealand macular hole and retinal detachment registry as well as the Australian breast device registry. The PhD candidate should ideally be someone who has a background in a quantitative field such as Statistics, Biostatistics or Mathematics. A PhD stipend could be made available to the right candidate. Essential Criteria: 1. Australian Citizen or Australian permanent resident. International students may apply but they would need to apply for a scholarship 2. An undergraduate (Honours) or Masters degree in Biostatistics, Statistics, Mathematics, Public Health or related discipline 3. High-level analysis skills; familiarity with Stata and/or WinBUGs program 4. Ability to work autonomously as well as collaborate with clinicians 5. Excellent written communication and verbal communication skills with proven ability to produce clear, succinct reports and documents 6. A demonstrated awareness of the principles of confidentiality, privacy and information handling 7. Well-developed planning and organisational skills, with the ability to prioritise multiple tasks and set and meet deadlines Interested candidates who meet the above selection criteria should contact Associate Professor Arul Earnest to discuss their suitability. Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum
Essential criteria: 
Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum
Keywords 
Biostatistics, registry, big data, data science
School 
School of Public Health and Preventive Medicine » Epidemiology and Preventive Medicine
Available options 
PhD/Doctorate
Time commitment 
Full-time
Part-time
Top-up scholarship funding available 
No
Physical location 
553 St Kilda Rd, Melbourne (adjacent to The Alfred)
Co-supervisors 
Dr 
Rohan Essex
(External)

Want to apply for this project? Submit an Expression of Interest by clicking on Contact the researcher.