Aarti Gulyani is a Senior Biostatistician at the Caring Futures Institute and the College of Nursing and Health Sciences, Flinders University. With over 15 years of experience as an applied biostatistician, she has worked with South Australian universities, national data registries, and the South Australian Health and Medical Research Institute.

Drawing on her strong background in statistics and extensive experience in health data analysis, Aarti provides high‑level statistical consultancy to researchers, helping them in navigating complex analyses of experimental and observational studies, advising on grant applications – particularly around sample size and power calculations, data analysis methods and assisting with the development of Statistical Analysis Plans. As the Senior Biostatistician, she also provides tailored data analysis services for researchers with limited statistical capacity. Work is currently underway to establish a dedicated Statistical Centre within the College.

Beyond consultancy, Aarti is deeply committed to mentoring and advancing the statistical capability of her peers. Through delivering statistical workshops, offering guidance on study design, advising on appropriate statistical models, helping the interpretation of analyses, and teaching undergraduate students to build their own statistical and research skills, Aarti provides expertise that is invaluable to both colleagues and students.

She is also pursuing her PhD, with research centered on estimating the relative survival of individuals with multiple cancers using population-based data. Her interest lies in advanced data programming (STATA) skills, and statistical modelling techniques, including mixed effect models, flexible parametric survival models, mixture cure methods, and machine learning.

Aarti is passionate about fostering collaboration, sharing her expertise, producing impactful publications, and advancing research excellence across university disciplines.

• Analysis of observational and experimental studies.
• Statistical Modelling (longitudinal data, repeated measures, survival analysis, mixed effect models, multistate models, zero-inflated data and meta analysis).
• Study design (Latin-square designs, Step-wedge trial, cluster randomised trial, sample size/power calculations, SAP).
• Epidemiology data and modelling
• Data quality, missing data and imputation techniques.
• Machine learning and predictive modelling.
• Analysis of PBS and MBS data and Data linkage studies
• Statistical coding and reproducible research workflows.