Statistics
What we do
QCIF Statistics is a dedicated group of applied statisticians and data scientists specialising in biostatistics, epidemiological methods, and data management.
Working closely with researchers and clinicians, our statisticians apply rigorous statistical methodology to study design, methods, and analyses through reproducible workflows, data wrangling, and visualisation. Drawing on clinical expertise and advanced training in statistics and data science, our team ensures that statistical insights are relevant, interpretable, and aligned with research goals.
Partnering with state and national groups, we support cutting-edge solutions and the advancement of methodological and collaborative research in health. We also engage directly with clinical researchers and academics through QCIF Statistical Clinics and outreach activities, supporting disciplines such as surgery, medicine and chronic diseases, anaesthesia, radiation oncology, rheumatology, women’s health, and paediatric diseases.
We help strengthen grant applications by providing methodological guidance and aligning study objectives with appropriate statistical methods, increasing the likelihood of approval.
Focus Area
Randomised Controlled Trials (RCTs) and Observational Studies
We provide guidance at every stage of the research process. From study design and sample size calculations through to data collection, analysis, and interpretation, our statisticians ensure that projects are methodologically robust, and results are reliable. By working closely with researchers, we help maximise the impact of their findings and strengthen the evidence base across diverse fields of study
Systematic Reviews/Meta-Analysis
Our team offers expert support for researchers conducting systematic reviews and meta-analyses, ensuring adherence to rigorous methodological standards and accurate evidence synthesis.
We assist with statistical modelling for meta-analysis, including effect size estimation, heterogeneity assessment, subgroup and sensitivity analyses, and publication bias evaluation.
Additionally, we guide the preparation of results for reporting and publication, enabling researchers in the biological and health sciences to present their findings with clarity, transparency, and in compliance with established reporting standards such as PRISMA and MOOSE.
Predictive Modelling and Machine Learning
QCIF statisticians also work with researchers on predictive modelling and artificial intelligence (AI) enquiries, applying advanced statistical and machine learning techniques to uncover patterns, forecast outcomes, and generate actionable insights
Factor Analysis
Our team supports researchers in conducting both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), ensuring robust identification and validation of underlying latent constructs.
We provide guidance on selecting appropriate factor extraction methods, rotation techniques, and model fit indices, as well as interpreting factor loadings and structural relationships to help researchers uncover hidden patterns, simplify data, and strengthen the interpretation of results.
