The purpose of this course is to build on the knowledge and skills obtained in Biostatistics A, and to introduce more complex methods of analyses which are commonly used in epidemiologic research.
- Semester 2 - 2017
- Semester 2 - 2017
On successful completion of the course students will be able to:
1. Understand and carry out analysis of variance and the non-parametric Kruskal-Wallis analogue (where appropriate) and interpret the results of these analyses
2. Understand problems with multiple tests using the same data and apply appropriate techniques to overcome such problems
3. Be able to calculate parametric and non-parametric correlation co-efficients and determine which method is appropriate
4. Use a statistical package to perform simple linear regression and multiple linear regression, to correctly interpret output from analyses and to check assumptions made
5. Use Chi square methods to test hypotheses about data summarised in an r x c contingency table
6. Use appropriate hypothesis tests for 2 x 2 contingency tables for a variety of study designs and to calculate and interpret estimates and confidence intervals, as appropriate, for measure of association for 2 x 2 tables
7. Understand and be able to apply, where appropriate, Mantel-Haenszel methods
8. Determine when it is appropriate to use logistic regression, and to be able to undertake logistic regression using a statistical package and interpret the output from logistic regression analysis
9. Determine when it is appropriate to use survival analyses, to obtain product-limit and life-table estimates of survival curves and to use the log-rank test to compare survival curves
10. To undertake proportional hazards regression using a statistical package and to interpret the output from the regression
11. Be able to choose appropriate methods of analysis from the statistical techniques covered in the Biostatistics (A and B) course.
The course builds on the knowledge and skills developed in Biostatistics A, BIOS6910. Topics include analysis of variance, correlation and linear regression, analysis of contingency tables, logistic regression and survival analysis. Although students are expected to undertake analyses using a computer package, the subject does not focus on computing skills, but instead on interpretation of output and the appropriate use of methods of analyses.
The learning modules comprising Biostatistics B are:
S5 - Statistics: Oneway ANOVA and multiple comparisons
S6 - Statistics: Regression, correlation and multiple regression
S7 - Statistics: Analysis of contingency tables
S8 - Statistics: Logistic regression
S9 - Statistics: Survival analysis
OV3 - Overview: Choosing an appropriate method of analysis
BIOS6910 - Biostatistics A
Quiz: Progress Assessment
In Term Test: Marked Assignment (x2)
In Term Test: Take home examination
Callaghan and WebLearn GradSchool
Online 2 hour(s) per Week for Full Term
Contact hours are an indication only.
Face to Face On Campus 2 hour(s) per Week for Full Term
Face to Face Tutorial 2hours a week for local students.