ADVANCED QUANTITATIVE METHODS II
2018/2019, Semester 2
Saw Swee Hock School of Public Health (Saw Swee Hock School of Public Health)
Modular Credits: 4
This module will be taught over 7 Wednesdays, from 9am to 5.30pm. The dates for the module are:
13 February 2019
20 February 2019
6 March 2019
13 March 2019
20 March 2019
27 March 2019
3 April 2019
Students will need to commit to either SPH6002 or SPH5101 from the point of module registration. Switching of modules from SPH6002 to SPH5101 or vice versa is not allowed. Please also note that
the deadline to drop the module is by 5pm of 13 February 2019
For more information on topics and venues, please check the module schedule uploaded in IVLE Files. Any changes to the schedule will be reflected in the module schedule.
In this module, the principles of advanced statistical modelling will be introduced, and statistical models such as multiple linear regression, logistic regression and Cox proportional hazards model will be applied to a variety of practical medical or public health problems. For time-to-event data analysis involving the Cox proportional hazards model, the proportional hazards assumption will be discussed, and strategies for handling non-proportional hazards, such as via stratification or modelling using time-dependent covariates will be introduced. We also consider the situation where several competing event types define the event of interest in a time-to-event study. Methods for analysing repeated measures data, assessment of model fit, statistical handling of confounding and statistical evaluation of effect modification will also be discussed. The statistical models introduced will be applied to real life clinical or public health data.
A minimum grade ‘B-’ obtained in CO5103 Quantitative Epidemiologic Methods OR SPH5002 Public Health Research Methods, and working knowledge of STATA.
Upon completion of this course, you will be able to:
Build statistical models for outcomes involving binomial, normal, survival or repeated measures data.
Discuss and test the validity of the assumption underlying each model.
Assess model fit, confounding and effect modification.
Interpret the effect estimates meaningfully and make appropriate inferences.
Discuss the uses and limitations of multi-variable analyses.
Apply the statistical models learnt to real life medical or health outcome data.
Independently analyse medical or public health research data.
Written report 1*
Written report 2**
Total for CA:
*Written report 1 is due for submission on
**Written report 2 is due for submission on
18 April 2019
With effect from AY2018/2019 Semester 1, all lecture recordings will be migrated to LumiNUS. To access the lecture recordings, please do the following:
Login to LumiNUS (
Click on the module code
Go to Tools --> Web Lectures
When a student is unable to attend the required sessions, an excuse may be granted for limited time periods upon the production of evidence of illness, misadventure or leave of absence having been granted.
Students must inform the Education Office if any of the above has taken place.
Failure to meet attendance requirements will affect module grading.