Available in 2013
|Distance Education - Callaghan||Semester 1|
Previously offered in 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004
Aims to enable students to understand the impact of computers and the corresponding availability of data sets (often very large data sets) on the way we think about data and proceed to analyse or report on it. Explores biostatistical applications of survival analysis with an emphasis on underlying theoretical and computational issues, practical interpretation and communication of results.
This course is offered in conjunction with the Biostatistics Collaboration of Australia (BCA).
|Objectives||At the completion of this course students should be able to:
1. understand the major theoretical and computational issues underlying survival analysis
2. develop appropriate survival analysis strategies based on course matter considerations, including choice of models, control for confounding and appropriate parameterisation
3. be proficient at using at least two different statistical software packages (eg Stata, Excel) to perform survival analysis
4. understand the construction, use and interpretation of appropriate graphs for showing results and checking statistical assumptions
5. express the results of statistical analyses of censored data in language suitable for (a) communication to medical investigators and (b) publication in biomedical or epidemiological journals
6. appreciate the role of newer techniques including parametric non-modelling, floating odds ratios and competing risks.
|Content||Through a series of case studies, students will explore the various methods for handling survival data. These begin with the Kaplan-Meier curve definition and its extension to the comparison of survival prospects of several groups of courses using the logrank test and confidence intervals for relative risks, emphasising graphical displays and assessing underlying assumptions. The connection between Mantel-Haenszel methods and survival analysis is thus emphasised. The Cox proportional hazards model is introduced as a method for handling continuous covariates. Various extensions of this model, including time-dependent covariates and multiple outcomes, are considered, as well as the censored linear regression model.|
|Assumed Knowledge||Epidemiology (EPID6420);
Mathematical Background for Biostatistics (BIOS6040);
Probability and Distribution Theory (BIOS6170);
Principles of Statistical Inference (BIOS6050);
Linear Models (BIOS6070).
|Modes of Delivery||Distance Learning : Paper Based|
|Teaching Methods||Email Discussion Group|
|Contact Hours||Self Directed Learning: for 8 hour(s) per Week for Full Term|
|Timetables||2013 Course Timetables for BIOS6030|