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HASC415 Regression Methods: Health Science Applications

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 HASC413, 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.

Paper title Regression Methods: Health Science Applications
Paper code HASC415
Subject Health Sciences
EFTS 0.125
Points 15 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees (NZD) $1,444.50
International Tuition Fees (NZD) $5,337.00

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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

Teaching staff

Associate Professor Robin Turner, Dr Jiaxu Zeng

Paper Structure
  1. Simple linear regression
  2. Multiple regression
  3. Effect modification and interactions
  4. Model building: variable selection
  5. Model diagnostics and robust standard error
  6. Logistic regression
  7. Poisson and negative binomial models
  8. Overdispersion
  9. Introduction to other types of regression methods
  10. 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.

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Timetable

Semester 2

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Moodle

Computer Lab

Stream Days Times Weeks
Attend
A1 Thursday 11:00-12:50 28-34, 36-41

Lecture

Stream Days Times Weeks
Attend
A1 Tuesday 14:00-15:50 28-34, 36-41

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.

Paper title Regression Methods: Health Science Applications
Paper code HASC415
Subject Health Sciences
EFTS 0.125
Points 15 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees Tuition Fees for 2022 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

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 - postgrad.psm@otago.ac.nz

Teaching staff

Associate Professor Robin Turner
Dr Jiaxu Zeng

Paper Structure
  1. Review of simple linear regression
  2. Multiple regression
  3. Model building and model diagnostics
  4. Introduction to causal inference
  5. Logistic regression
  6. Logistic regression: extension
  7. Poisson regression
  8. Overdispersion and negative binomial models;
  9. Model diagnostics for discrete data
  10. Introduction to other type of regression models
  11. 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.

^ Top of page

Timetable

Semester 2

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Moodle

Computer Lab

Stream Days Times Weeks
Attend
A1 Thursday 11:00-12:50 28-34, 36-41

Lecture

Stream Days Times Weeks
Attend
A1 Tuesday 14:00-15:50 28-34, 36-41