Dr. Siswanto Agus Wilopo,SU,MSc,ScD
Department of Public Health

This course offers continuing education units (CEU’s). For those successfully completing the course (generally this means marks of B or better on the homework), is equal to 3 credits and a certificate will be issued by  Postgraduate Program of Public Health, Faculty of Medicine, Gadjah Mada University, upon request

Aim of Course:

To learn analyze data from studies in which individuals are  followed  up  until  a  particular  event  occurs,  e.g. death, cure, relapse, making use of follow-up data also for those who do not experience the event.
This  course  describes  the  various  methods  used  for modeling and evaluating survival data, also called time-to-event data or  time  failure analysis. To    field of engineering,  where  it  is  typically  referred  to  as  reliability analysis.
To use a convenient statistical package (e.g., STATA, R, or S+) to analyze survival analysis data. It  is desirable    their own  clean data  sets  for  the exercise during the workshop.

Learning Objectives:

At the end of this course students will be able to:

  • identify when it is appropriate to use survival analysis methods
  • define censoring, survivor function and hazard function
  • calculate a life table for a follow-up study with individuals grouped in time intervals according to how long they survived, allowing for censoring
  • calculate a life table for individual survival times using the product-limit (Kaplan-Meier) method
  • identify when it is appropriate to use the logrank method and Cox’s proportional hazards model
  • write computer programs in STATA or R to obtain life tables, perform logrank tests and fit Cox proportional hazards models, and interpret output from these programs
  • adjust for qualitative covariates in a logrank test
  • check the proportional hazards assumption and describe what to do if it does not hold
  • use tables or computer programs to determine the required sample size for a follow-up study that will use survival analysis

Course Program:

Module 1:
An overview of survival analysis methods with many examples
Key terms: survival and hazard functions
Goals of a survival analysis
Data layout for the computer
Data layout for theory
Descriptive statistics for survival analysis- the hazard ratio
Graphing survival data- Kaplan Meier

Module 2:
Introduction to the Cox Proportional Hazards (PH) model- computer example
Model definition and features
Maximum likelihood estimation for the Cox PH model
Computing the hazard ratio in the Cox PH model
The PH assumption
Adjusted survival curves
Checking the proportional hazard assumption

Module 3:
Introduction to the Stratified Cox procedure
The no-interaction Stratified Cox model
The Stratified Cox model that allows for interaction
Definition and examples of time-dependent covariates

Module 4:
Definition and features of the extended Cox model
Stanford Heart Transplant Study Example
Addicts Dataset Example
The likelihood functions for the extended Cox model.
Sample size estimation for a follow-up study that will
use survival analysis

The first batch of the course will be started in
3 December 2010 for two weeks. Participants
are required to be present in the class for 2 days
at every week (Friday and Saturday).
Next  course will be announce later


Participants  should  register  by  email  or  phone  in the following address:
Ms. Utami Dwiastuti/Ms. Antini Kurniawati
Maternal  and  Child  Health  –  Reproductive  Health Division, Department of Public Health
Faculty of Medicine, Gadjah Mada University
IKM Building 1st Floor, Room 110
Phone/Fax (0274)565076 or 548156

16 hours class per week (two days class)

COURSE Fee: Rp. 500.000

Download leaflet pdf

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: