PHCA9524 Advanced Biostatistics

This topic is ideal for those who wish to further develop their quantitative research skills using advanced biostatistical techniques. It improves their literacy in reading and critiquing methodology for journal articles in medicine and public health. The focus of the topic is very applied and not mathematical. Students will gain R programming experience applying model-building strategies and fitting advanced statistical models.

This topic aims to enable you to apply advanced biostatistical methods to address public health research questions. In particular, it aims to support you to reach a level of proficiency where you will be able to select the appropriate statistical analytical method to address specific research questions with a given data set, conduct the selected statistical analysis using R programming, present and interpret the results appropriately, and draw valid and insightful conclusions about the research question(s).

1. Classify data into appropriate measurement types
2. Conduct statistical analysis using statistical techniques on health datasets with different types of variables
3. Demonstrate an understanding of issues arising from the application of modelling techniques in statistical analysis and appropriate procedures to handle these issues. Key issues included confounding and effect modification in epidemiological studies, model building strategies and model diagnostics
4. Demonstrate practical skills in fitting and interpreting regression models for count data or event rates (Poisson and Negative Binomial Regression)
5. Understand the basic concepts of survival analysis including the survival function, the hazard function, survival curves based on the Kaplan-Meier estimator, log rank test, univariate and multivariate Cox Proportional Hazards model
6. Describe the importance of statistical power and determine the appropriate sample size when planning a research study
7. Construct functioning scripted statistical analysis by using R programming to load, wrangle, and analyse a dataset
8. Correctly interpret results and draw valid conclusions addressing the research question
9. Critically discuss results and present findings at a standard that is sufficient for submission to scientific journals or reports