Overview
The use of regression methods (e.g. linear, logistic, and Poisson regression) for answering scientific questions in the health sciences. Topics include fitting/interpreting regression models and scientific issues in their application (e.g. outcome parameterisation, model selection, missing data).
This paper builds on the materials of HASC 413, and introduces advanced regression methods for health-related research. Topics include multiple regression for continuous and discrete response variables, model building and diagnostics, control for confounding and interaction.
About this paper
| Paper title | Regression Methods: Health Science Applications |
|---|---|
| Subject | Health Sciences |
| EFTS | 0.125 |
| Points | 15 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,509.38 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- HASC 413
- Restriction
- PUBH 726, STAT 241, STAT 341
- Limited to
- MHealSc, PGDipHealSc, PGDipSci, MSc
- Notes
- The prerequisite may be waived for students with an equivalent level of knowledge.
- Eligibility
Suitable for students from any disciplines who are interested in learning advanced regression methods.
- Contact
Administrator: Amara Boyd - researchstudentadmin-psm@otago.ac.nz
- Teaching staff
- Paper Structure
- Review of simple linear regression
- Multiple regression
- Model building and model diagnostics
- Introduction to causal inference
- Logistic regression
- Logistic regression: extension
- Poisson regression
- Overdispersion and negative binomial models
- Model diagnostics for discrete data
- Introduction to other types of regression models
- Introduction to missing data
- Textbooks
E. Vittinghoff, D.V. Glidden, S.C. Shiboski, C.E. McCulloch. Regression methods in biostatistics linear, logistic, survival, and repeated measures models. Springer-Verlag, New York (2005).
- Graduate Attributes Emphasised
Communication, Critical thinking, Research.
View more information about Otago's graduate attributes.- Learning Outcomes
By the end of the course, students are expected to carry out appropriate regression analyses to answer health research questions and gain skills in modelling fitting, model selection and interpretation of results.
Timetable
Overview
The use of regression methods (e.g. linear, logistic, and Poisson regression) for answering scientific questions in the health sciences. Topics include fitting/interpreting regression models and scientific issues in their application (e.g. outcome parameterisation, model selection, missing data).
This paper builds on the materials of HASC 413, and introduces advanced regression methods for health-related research. Topics include multiple regression for continuous and discrete response variables, model building and diagnostics, control for confounding and interaction.
About this paper
| Paper title | Regression Methods: Health Science Applications |
|---|---|
| Subject | Health Sciences |
| EFTS | 0.125 |
| Points | 15 points |
| Teaching period | Not offered in 2024 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2024 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- HASC 413
- Restriction
- PUBH 726, STAT 241, STAT 341
- Limited to
- MHealSc, PGDipHealSc, PGDipSci, MSc
- Notes
- The prerequisite may be waived for students with an equivalent level of knowledge.
- Eligibility
Suitable for students from any disciplines who are interested in learning advanced regression methods.
- Contact
Administrator: Amara Boyd - researchstudentadmin-psm@otago.ac.nz
- Teaching staff
- Paper Structure
- Review of simple linear regression
- Multiple regression
- Model building and model diagnostics
- Introduction to causal inference
- Logistic regression
- Logistic regression: extension
- Poisson regression
- Overdispersion and negative binomial models
- Model diagnostics for discrete data
- Introduction to other types of regression models
- Introduction to missing data
- Textbooks
E. Vittinghoff, D.V. Glidden, S.C. Shiboski, C.E. McCulloch. Regression methods in biostatistics linear, logistic, survival, and repeated measures models. Springer-Verlag, New York (2005).
- Graduate Attributes Emphasised
Communication, Critical thinking, Research.
View more information about Otago's graduate attributes.- Learning Outcomes
By the end of the course, students are expected to carry out appropriate regression analyses to answer health research questions and gain skills in modelling fitting, model selection and interpretation of results.