Practical Biostatistical Analysis Using Stata

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.

 
Level 2 - runs over 5 days
Instructor: 

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.

Course dates: Monday 4 July 2016 - Friday 8 July 2016
Course status: Course completed (no new applicants)
Week: 
Week 2
About this course: 

An integral part of biomedicine is unravelling the different sources of variation related to response to stimuli. Subjects under investigation can be patients, laboratory animals or even at the cellular level.

 

Central to biostatistical techniques is the aim to distinguish chance occurrences and possible causal associations, and to make valid inferences from known samples about the populations from which they were drawn. Bioastatistical data is evaluated as scientific evidence, where a mathematical framework is used to generalise findings. Identifying the right study design method and the application of mathematics are techniques used by biostatisticians to enhance their science and bridge the gap between theory and practice.
This course is designed for participants with a basic understanding of statistics and builds up over the week to more sophisticated biostatistical techniques. The course provides a grounding in the use of the Stata package for biostatistical techniques, focusing on the analysis of medical data. Descriptive statistical techniques are used to demonstrate the package's features.

 

This expanding branch of statistics is extensively used in areas such as biology, health policy, clinical medicine, health economics, genomics and public health policy.

Course syllabus: 

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

 

Day 2

  • Study design methods

               Experimental design
               Two and Three Factorial experiments
               Designed experiments
               Measures of association and impact

  • Summarizing data

               Sampling and inferential statistics
               Frequency and frequency distributions
               The use of graphs to describe data
               Population proportions and percentiles
               Mean, standard deviation, median and interquartile range

  • Transformations

 

Day 3

  • Statistical reliability and confidence intervals

               Sampling distributions, unbiased estimators and accuracy
               The Normal distribution
               Standard error and confidence intervals

  • Testing statistical hypotheses

                Hypothesis testing, p-values and significance
                Comparing groups – continuous data, using paired and two sample t tests
                Comparing groups – categorical data, using chi-squared tests

 

Day 4

  • Linear Regression

               Bivariate data, scatterplots and correlation
               Simple linear regression
               Statistical inference
               Investigating multivariate relationships
               Multiple linear regression
               Confounders and interactions
               Goodness of fit and regression diagnostics

  • Logistic regression

               Probability, relative risk and odds ratio
               Comparing proportions
               Multiple logistic regression
               Goodness of fit and regression diagnostics

 

Day 5

  • Survival analysis

               Kaplan-Meier survival functions
               Cox proportional hazards regression
               Checking assumptions

Course format: 

This course will take place in a computer lab and uses the Stata software. Notes and data files will be provided.

Recommended Background: 

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.

Course fees
Member: 
$1,950
Non Member: 
$3,700
Full time student Member: 
$1,930
FAQ: 

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.

Program: 
Winter Program 2016
Notes: 

The instructor's bound, book length course notes will serve as the course texts.

Supported by: 

Stata is distributed in Australia and New Zealand by Survey Design and Analysis Services.