Biostatistics is the statistical analysis of biomedical and health care data generated from the health sciences. This course has a practical hands on structure, with exercises for each topic to allow participants time to discuss and digest the material covered.
Dr Joanna Dipnall is an applied statistician with interests in the advanced statistical methods, including machine learning and deep learning techniques. She completed her Honours in Econometrics with Monash University and her PhD with IMPACT SRC, School of Medicine, Deakin University. Joanna works extensively with registry and linked medical data and collaborates extensively with the Faculty of IT at Monash to supervise Masters and PhD students to integrate artificial intelligence within health research. Joanna teaches within the Monash Biostatistics Unit and is the Unit Co-coordinator for the Monash Masters of Health Data Analytics course. Joanna has taught advanced statistical methods for many years at universities and for ACSPRI.
This course is designed for participants with a basic understanding of statistics and builds up over the week to more sophisticated biostatistics. The course provides a grounding in the use of the Stata package for biostatistics, focusing on the analysis of medical data. Descriptive statistical techniques are used to demonstrate the package's features.
This workshop will provide a practically oriented introduction to a range of modern statistical methods that are commonly used for analysing medical data from epidemiological or clinical studies (e.g. cross-sectional, cluster randomised trials, longitudinal cohort studies). The last day will finish with a range of exercises to practice implementing the week’s learnings.
Day 1
- Introduction to biostatistics
- Talking about populations
Population distributions and parameters
Probability and probability models
- Representative data
Different sampling plans
Power and sample size
- Summarizing data (part 1)
Sampling and inferential statistics
Frequency and frequency distributions
Day 2
- Summarizing data (part 2)
Population proportions and percentiles
Mean, standard deviation, median and interquartile range
Visualising your data
Transforming data
- Testing statistical hypotheses
Hypothesis testing, p-values and significance
Comparing groups – continuous data, using paired and two sample t tests, oneway ANOVA
Comparing groups – categorical data, using chi-squared, Fisher’s exact tests
- Linear Regression
Simple linear regression
Investigating multivariate relationships
Multiple linear regression
Confounders and interactions
Goodness of fit and regression diagnostics
Day 3
- Logistic regression
Probability, relative risk, absolute risk and odds ratio
Comparing proportions
Multiple logistic regression
Goodness of fit and regression diagnostics
- Poisson regression
Incidence rate ratios
Multiple poisson regression
Poisson regression diagnostics
Day 4
- Survival analysis
Kaplan-Meier survival functions
Cox proportional hazards regression
Checking assumptions
- Panel data
Preparing panel data in Stata
Balanced versus unbalanced panels
Fixed, between and random effects
Panel regression diagnostics
Day 5
- Group exercises
This course will take place in a computer lab and uses the Stata software. Notes and data files will be provided.
The course assumes that participants have some familiarity with a PC environment including keyboard skills and understanding of folder and file structures; some experience in using Microsoft Word and Excel or their equivalent.
Q: Do I have to have any prerequisites to do this course?
A: No, but it would help to have to have some experience with a PC based environment and a basic statistical knowledge.
The instructor's bound, book length course notes will serve as the course texts.
Stata is distributed in Australia and New Zealand by Survey Design and Analysis Services.