Registry Data Analysis and Reporting
Monash Clinical Registries utilise the expertise of senior and experienced statisticians and data analysts for all formal reporting and more complex research-related activities. Subscribing to the principles of Reproducibility, Accuracy, Consistency and Validity (RACV), the team also work closely with registry practitioners via a hub and spoke model, to elevate the level of statistical literacy among staff.
Specific activities include:
- Providing statistical support to produce annual reports which are statistically sound and in a timely manner
- Troubleshooting existing data analytics problems
- Developing guidelines and operating procedures for the design and analysis of registry data
- Undertaking quality control audits of data/analysis from registry data
- Developing descriptive and inferential dashboards
- Automating annual and benchmarked site reports
- Planning and conducting training courses and workshops
The team leads methodological and applied research, including simulation studies to improve the application of outlier detection methods for rare/sparse disease outcomes, geo-spatial Bayesian hierarchical models for areal data, risk-adjusted cusum models, machine learning techniques to predict and classify health outcomes and reporting checklist for registry data. The team regularly conducts workshops on ‘Clinical Registry Data Analysis using Stata’ and is currently planning newer workshops on machine learning, Bayesian statistics, developing dashboards and automating reports using R.
Our Team
Senior Biostatistician
- Professor Arul Earnest
PhD, MSc, DLSHTM, BSocSc(Hons)
Senior Data Analyst
- Dr Ahmad Reza Pourghaderi
PhD, MScSCM, BEngISE
Data Analysts
- Dr Mohammad Amin Honardoost
PhD, MSc, BSc - Patrick Garduce MBiostat, BBiomedSc(Hons), BSc
- Jessy Hansen MEpi, BSc
PhD students
- Jessy Hansen MEpi, BSc
- Zemenu Tessema MPH, MSc, BSc