ADVANCED QUANTITATIVE METHODS II
2016/2017, Semester 2
Saw Swee Hock School of Public Health (Saw Swee Hock School of Public Health)
Modular Credits: 4
The module will be offered as an intensive module in Semester 2, from 23 - 27 January 2017 and from 31 January - 2 February 2017. Classes will be conducted from 9am - 6pm daily except on the following date:
On 27 January 2017, the class will start at 8.30am and end at 1pm
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 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
10 Feb 2017
**Written report 2 is due for submission on
17 Feb 2017
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