Programmes by Topic

This advanced course expands students’ econometric toolset in two key areas of HEOR: (1) modelling discrete outcomes, such as number of admissions and lengths of stay, and (2) modelling censored and non-normally distributed outcomes, such as medical expenditures. Estimation of these outcomes requires a movement away from traditional ordinary least squares (OLS) regression analysis. This course will focus on generalized linear models, count models, and models that deal with data with mass at zero or with long tails. This course will also expand on techniques for causal inference, including fixed/random effect panel models, difference-in-differences, and regression discontinuity designs in efforts to minimize biases inherent in real-word evidence data.

By the end of the course, you will be able to:

• Apply advance Stata programming skills to deal with econometrics issues in real-world data  
• Choose model specificiations for given data and research questions of interest
  

 

This course is part of the Graduate Certificate in Health Economic Evaluation

 

As part of the micro-credential initiative, you may choose to take this course alone if you do not wish to take the full Graduate Certificate program.

Course Fees

This course costs SGD5,400 before any subsidy. Do enquire with your CV so that we can evaluate the subsidy quantum.

Note

1. Student Service Fee (SSF): Students are required to pay an additional SGD25.70 per semester. In the event that students do not take any courses that semester, they would not have to pay this fee.
   
2. Please note that the Tuition Fee and Student Services Fee (SSF) are non-refundable and non-transferable.
3. All requests for withdrawal must be received 6 weeks before the start of the workshop for the course. Late withdrawals will be subjected to payment of the FULL course fees. Any access to learning materials (online preparatory materials and workshop materials) will be considered as participation and no refunds will be allowed.

Do contact us at lifelonglearning@duke-nus.edu.sg for any enquiries.