Clinical Registry Data Analysis Using Stata - PDM1119

DAY 1

  • The first day includes an introduction to Stata and data management topics such as recoding and computing variables, reshaping and merging datasets.
  • This will be followed by statistical tools and techniques that can be utilised to report data from registries.

DAY 2

  • The second day follows through with more advanced statistical models that would be useful for longitudinal data such as Generalised Estimating Equations (GEE) and time series analysis using Autoregressive Integrated Moving Average (ARIMA) models.
  • Finally, advanced graphics such as risk-adjusted funnel plots, cusum plots will be taught.

Each lecture will be followed by hands-on practical sessions using realistic clinical registry data, and the sessions are meant to be highly interactive.

Participants will be provided with a Stata training license.

Topics covered

  • Importing data into Stata, label variables, recode and compute variables, merging and reshaping datasets, simple descriptive statistics Reporting data from registries
  • Longitudinal analysis of registry data – generalised estimating equation (GEE) modelling
  • Longitudinal analysis of registry data – ARIMA modelling
  • Advanced graphics – funnel plots, cusum charts, survival plots etc

Presenter:

A/Prof Arul Earnest, who works in the School’s Biostatistics, Data Analytics/Modelling and the Health Economics Division & Clinical Outcomes data Reporting and Research Program.

Additional course information

Participants may register for one or both days of this course

Fees

1 day: $550 incl. GST 2 days: $1,100 incl. GST

Alumni discounted fees

10% discount for Student/ Alfred staff/ Monash staff/ Alumni

A/Prof Arul Earnest
PhD (Brisbane), MSc (London), DLSHTM, B.Soc.Sc (Hons)
Associate Professor, Biostatistics Unit
Senior Biostatistician, Registry Sciences Unit

Arul is currently an Associate Professor with the Biostatistics Unit and senior Biostatistician with the Registry Sciences Unit in the Department of Epidemiology and Preventive Medicine. He was previously Director of the Centre for Quantitative Medicine (CQM) at Duke-NUS in Singapore. His current research interest is in risk adjustment and time series analysis of clinical registry data and Bayesian spatio-temporal models. He has developed and applied these models on a number of clinical registries in Australia and internationally.

Please click here for more information about the course director.

This course is for anyone involved in the design, conduct and analysis of registry/longitudinal data. This includes clinicians, data analysts, health managers and research assistants.

The last Clinical Registry Data Analysis using Stata course taught by A/Prof Arul Earnest received an average rating of 4.65/5 on the participant evaluation form assessing the overall quality of the course.

Quote from a past participant:

‘All the speakers are very good and very supportive and kind. Very worthwhile, thanks!’