Research interests
Our department conducts research in a range of epidemiological and biostatistics fields. Click on the names of individual researchers to find out more about their interests and publications.
Our researchers
- Biostatistical consulting
- Statistical methodology
- Analysis of health data
- Programming in Stata, R and SPSS
- Randomised controlled trials
- Regression modelling
- Sample size calculations
- Environmental epidemiology
- Climate change
- Transport
- Road safety
- Air quality
- Biostatistical consulting
- Statistical methodology
- Analysis of health data
- Programming in SAS and R
- Clinical trials
- Regression modelling
- Routinely collected administrative data
- Obesity prevention
- Community-based interventions
- Food policy
- Food systems
- Systems science approaches
- Population nutrition
- Dietary interventions
- Nutrition labelling
- Food composition and nutrient profiling
- Dietary assessment methods
- Public Health Nutrition
- Equity
- Food environments
- Food policy
- Mobile health
- Nutrition interventions to lower blood pressure
- Children’s nutrition
- Environmental health
- Health equity
- Transport and health
- Mathematical modelling
- Climate change
- Health geography
- Spatial methodology
- Geographic Information System
- Mapping and visualisation
- Curate data for projects in population health
- Programming in R and SAS
- Extract data from Stats NZ’s Integrated Data Infrastructure (IDI)
- Pacific health
- Arterial function parameterisation and clinical utility
- Epidemiology, biostatistics, programming and data analysis
- Nutritional epidemiology
- Healthy and sustainable diets
- Vegetarians, vegans and animal-source foods
- Nutrition surveys
- Public health nutrition
- Monitoring of food composition and food environments
- Nutrition interventions
- Food and nutrition environments in education environments
- Food and nutrition policy
- Epidemiology of cancer
- Screening for disease
- Patient outcomes and quality of care
- Epidemiological methods
- Vitamin D and health
- Arterial function and prediction of macrovascular and microvascular disease
- Medical history
- Epidemiology (particularly of vascular diseases)
- Utilising big health data
- Vascular risk prediction
- Equity in vascular health
- Evidence Based Medicine methods
- Population health
- Study design in epidemiology
- Cohort studies
- Survival analysis
- Clinical risk prediction
- Epidemiology of chronic diseases
- Aetiological research
- Youth health
- Asian and ethnic minority health
- Population health
- Equity
- Intersectionality
- Mixed methods research
- Digital health
- mHealth
- Artificial intelligence (AI) governance
- Consumer perspectives of secondary data use
- Mixed methods research
- Public health nutrition
- Monitoring of food environments
- Sustainable diets
- Nutrition surveys
- Restricting marketing of unhealthy food to children
- Food composition
- Wholegrains
- Epidemiology of cancer and other chronic diseases
- Transport and health
- Physical activity
- Biomarkers
- Patient outcomes and quality of care
- Equity
- Cohort studies
- Routinely collected administrative data
- Epidemiological methods
- Epidemiology
- Biostatistics
- R programming
- Study design
- Teaching
- Scabies and skin issues
- Rheumatic heart disease
- Sugar, carbohydrates and metabolic health
- Nutritional epidemiology
- Diet quality in childhood: measurement, determinants and impact on health and wellbeing
- Monitoring of population nutrition
- Monitoring of food environments
- Clinical epidemiology
- Equity
- Public health
- Quality improvement
- Routinely collected administrative data
- Mental health
- Sexual and reproductive health
- Migrant health
- Asian and other ethnic minority health
- Public health
- Evidence synthesis
- Qualitative research
- Co-design approach
- Biostatistics
- Clinical trials
- Constrained maximum likelihood estimation
- Programming in R and SAS
- Statistical methodology
- Survey research
- Two-phase sampling designs
- Biostatistical consulting
- Statistical methodology
- Routinely collected data for patient care
- Prediction modelling
- Digital health at emergency department
- Programming in SAS