96706 Advanced Biostatistics
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Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 92974 Fundamentals of Biostatistics OR 96730 Fundamentals of Biostatistics
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
This subject is designed to extend students' knowledge and learning for a range of statistical methods, providing in-depth knowledge of three commonly used regression models, namely linear regression, logistic regression, and proportional hazards (Cox) regression. Concepts, such as interpretation of regression model output, model building strategies, assessment of model fit, and model diagnostics, are explored. Practical, hands-on experience of data analysis using a statistical computer package support further learning and skill development in this area.
Subject learning objectives (SLOs)
| A. | Discuss key elements of a number of regression models commonly used and encountered in the health/medical literature |
|---|---|
| B. | Determine when it is appropriate to use particular regression models |
| C. | Demonstrate an ability to undertake regression modelling using a statistical package |
| D. | Articulate the philosophy and practice of the regression approaches to data analysis |
| E. | Critically examine the regression models to ensure they meet required assumptions |
| F. | Correctly interpret output from analyses |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the following graduate attributes:
- Critique, interpret and synthesise epidemiological outcomes and statistical findings at a superior level to inform the surveillance, management and prevention of disease and illness, and promotion of health for the complex issues inherent in public health (1.1)
- Design and apply research methods to a variety of public health problems (1.2)
- Communicate and collaborate to provide optimal outcomes in public health practice and research (4.1)
Contribution to the development of graduate attributes
This subject also contibutes specifically to the following graduate attributes:
- Demonstrate reflective critical thinking to enable critical appraisal of current practice, policy and research with the aim to enhance health care and healthcare outcomes, and transform health (1.0)
- Apply research methods to a variety of public health problems (1.2)
- Justify and demonstrate knowledge and skills necessary to provide leadership on matters critical to public health (2.1)
- Communicate and collaborate to provide optimal outcomes in public health practice and research (4.1)
Teaching and learning strategies
In this subject students will participate in a number of teaching and learning activities that are designed to actively engage students to develop learning in Advanced Biostatistics principles and scholarship. Students will attend three days of on-campus workshops. Prior to the on-campus workshops, students will be required to read set texts (eg. book chapters, journal articles) which are essential background knowledge for the on-campus activities. The on-campus learning activities will include seminars and oral presentations, round table discussions and problem-based learning activities that will involve analysing ‘real world’ data sets under the supervision of highly experienced biostatisticians. Throughout the session, students will also be provided with additional material online (Canvas) (eg. journal articles, webcasts, website links) and online discussion will be used to further clarify lecture material. The online activities will take 2-3 hours per week, to prepare students for the assessment tasks in a phased and iterative manner. Early feedback will be provided on moderated discussion board activities and assessment will be explained in the face-to-face workshops as well as via the discussion board.
Content (topics)
Topic 1: Linear regression
- Introduction to regression analysis
- Formal statement of the model
- Descriptive measures of association between two variables
- Multiple linear regression
- Regression diagnostics and measures of model adequacy in multiple linear regression
- Model building
Topic 2: Logistic regression
- Introduction to logistic regression
- Interpretation of the coefficients of the logistic regression model
- The multiple logistic regression model
- Assessing the fit of a logistic regression model
- Model building strategies
Topic 3: Proportional hazards (Cox) regression
- Introduction to proportional hazards regression
- Interpretation of the coefficients of the proportional hazards regression model
- The multiple proportional hazards regression model
- Diagnostics and measures of model adequacy in proportional hazards regression
- Model building strategies
Assessment
Assessment task 1: Take home examination (1-week to complete)
| Intent: | The purpose of this assessment item is to assess students' ability to conduct basic and more advanced statistical analyses, as well as demonstrate an ability to correctly interpret the results of the analyses. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): C, D and F This assessment task contributes to the development of graduate attribute(s): .0, .0 and 1.2 |
| Weight: | 25% |
| Length: | 1,000 words |
Assessment task 2: Statistical analysis of continuous and binary data
| Intent: | The purpose of this assessment item is to assess students' ability to conduct advanced regression modelling and undertake appropriate regression diagnostics, as well as correctly interpreting regression model output. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): B, C, E and F This assessment task contributes to the development of graduate attribute(s): .0, .0, 1.1 and 1.2 |
| Weight: | 35% |
| Length: | 1,000 words |
Assessment task 3: Consulting report
| Intent: | The purpose of this assessment item is to assess students' ability to use a proportional hazards regression model to correctly analyse time-to-event data and undertake appropriate diagnostic tests of the regression model, as well as their ability to write a statistical report suitable for a non-statistician. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): A, C, D, E and F This assessment task contributes to the development of graduate attribute(s): .0, .0, .0, 1.2 and 4.1 |
| Weight: | 40% |
| Length: | 1000-1500 words (main report) + technical appendix |
Recommended texts
PEZULLO, J.C. (2013). Biostatistics for dummies. John Wiley & Sons, Inc., Hoboken, N.J. (available online via UTS library)
DUPONT, W.D. (2009). Statistical modeling for biomedical researchers. Cambridge University Press, Cambridge, U.K.
KIRKWOOD, B. R., and STERNE, J. A. C. (2003). Essential medical statistics. Blackwell Science, Malden, Mass.
Other resources
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