ADVANCED QUANTITATIVE METHODS II
2017/2018, Semester 2
Saw Swee Hock School of Public Health (Saw Swee Hock School of Public Health)
Modular Credits: 4
The module will be taught on Monday and Thursday mornings from 9am – 12noon for the lectures and 9 am – 1 pm for the practicals. Classes will commence on 29 January 2018.
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:
1. Build statistical models for outcomes involving binomial, normal, survival or repeated measures data.
2. Discuss and test the validity of the assumption underlying each model.
3. Assess model fit, confounding and effect modification.
4. Interpret the effect estimates meaningfully and make appropriate inferences.
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 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
13 April 2018
Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week
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.