090021 Advanced Biostatistics
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Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 090012 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:
- 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)
- Critique, interpret and synthesise data and research findings to inform the surveillance, management, prevention of disease and illness and promotion of health for the complex issues inherent in public health (1.1)
- Apply research methods to a variety of public health problems (1.2)
- Communicate effectively and appropriately in challenging, complex and diverse situations (4.0)
- Communicate and collaborate to provide optimal outcomes in public health practice and research (4.1)
- Embody the international standard of professional qualities appropriate to the scope of their role in regional, national and global health (5.0)
Contribution to the development of graduate attributes
This subject also contibutes speciically 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
This subject is delivered through weekly online learning activities over a period of six weeks. There is an expectation that students complete all online activities to get the most out of the subject. Students will be provided with engaging learning activities to deepen their understanding of biostatistical reasoning and its application relevant to health care and public health. Online activities will offer students with the opportunity to conduct statistical analyses, interpret the results, and engage with statistical software. Students are expected to work independently and to engage with other students and teachers online through discussion forums and other interactive activities such as weekly Zoom meetings.
Assessments are designed to complement the learning by providing students with the opportunity to practically apply and track their understanding of fundamental concepts and techniques relevant to biostatistics. Students will receive feedback from teaching staff in online discussion and activities and on assessment tasks.
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: Introductory regression analysis
| Intent: | The purpose of this assessment item is to assess students ability to conduct basic statistical analyses as well as 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): 1.0, 1.2 and 4.0 |
| 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): 1.0, 1.1, 1.2 and 4.0 |
| 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 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): 1.0, 1.2, 4.0, 4.1 and 5.0 |
| Weight: | 40% |
| Length: | 1000-1500 words (main report) + technical appendix |
Recommended texts
Kirkwood, B., and Sterne J. (2003). Essential medical statistics. Blackwell Science.
Vittinghoff, E. , Shiboski, S., Glidden, D. V. , and McCulloch, C.E. 2005. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer.
Both books are available online through the UTS Library.
Other resources
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