This course introduces the principles of advanced statistical modelling, and applies statistical models such as multiple linear regression, logistic regression and Cox proportional hazards model to a variety of practical medical or public health problems. It further discusses strategies for handling non-proportional hazards such as via stratification or modelling using time-dependent covariates, for time-to-event data analysis involving proportional hazards assumption. Regression models where several competing event types define the time-to-event of interest will also be considered. It further addresses methods for analysing repeated measurement data, assessment of model fit, handling of confounding and evaluation of effect modification. Upon completion of this course, you will be able to: 1. Build statistical models for outcomes involving binomial, normal, survival or repeated measures data. 2. Summarise and interpret the effect estimates in the context of real life medical or public health data. 3. Assess model fit, confounding and effect modification. 4. Test the validity of the assumption underlying each model. 5. Discuss the uses and limitations of multi-variable analyses. 6. Apply the statistical models learnt to real life medical or health outcome data. 7. Independently analyse and infer the results based on the models generated.