I was fortunate enough to become involved in health services research immediately after graduating. I not only found the work very interesting and rewarding but was humbled when I realised that the output from our research was used to direct health policy and that I was a part of a group that was having a positive impact on the community.
The statistical techniques I’ve been using enable us to draw conclusions in the form of causal relationships from observational data. This is an incredibly powerful set of analytical tools that provide insights that were, until now, hidden from us. As most health related data is observational, the application of these techniques across all health-related areas can provide enormous benefits that will improve our overall quality of life
Xenia Dolja-Gore has been a research statistician for over 20 years. She began working with health services research after completing a Bachelor of Mathematics at the University of Newcastle. The aim of her work was to perform health-related research with particular emphasis on the development and application of quality/clinical indicators in the health industry.
In 2006 she became involved with the Australian Longitudinal Study on Women’s Health project through the Research Centre for Gender, Health and Ageing (RCGHA) located within HMRI. Her work includes the development and application of statistical techniques to explore and analyse health data from a number of linked longitudinal and cross-sectional data sources.
In 2009-2011 Ms Dolja-Gore was the lead statistician on a NHMRC project to examine the effects of the discrediting of certain medications and the then standard treatment, Hormone Replacement Therapy. Her recent research involves examining health service use for women age 75 years and over.
Ms Dolja-Gore has published the results of her research in current medical journals and presented at several national and international conferences. She is currently finalising her PhD entitled “Modifiable predictors of mental health service utilisation for Australian Women”.
Statistical analyses including multi-level modelling, propensity scoring, GEEs, data presentation, scientific writing and statistical programming in SAS, S-Plus, M-Plus, SPSS and JMP.
