All models are wrong, but some are useful. Developing a useful statistical model from the available data can be challenging! For example, what should you do if a model assumption is violated, or if data are missing? Your statistical toolbox will be expanded to include modern techniques for tackling challenging issues that often exist in health research data, e.g. missing observations, non-linear effects, confounding and correlation between observations in a dataset. The methods for correlated data are relevant for analysing some epidemiological observational study designs (e.g., matched case-control studies, longitudinal studies with repeated measurements), and clinical trial designs (e.g. cluster RCTs, cross-over RCTs). Techniques to help assess the usefulness of a model will also be covered. This unit of study focuses on the application of statistical methods using the statistical software R. Topics include: fractional polynomials for non-linear effects; mixed or random effects and marginal models (e.g. GEE) for correlated data; multiple imputation for missing data; propensity score for confounding; tools to assess model performance and classification.
Unit details and rules
Academic unit | Public Health |
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Credit points | 6 |
Prerequisites
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PUBH5217 |
Corequisites
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None |
Prohibitions
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CEPI5310 |
Assumed knowledge
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None |
Available to study abroad and exchange students | No |
Teaching staff
Coordinator | Shuvo Bakar, shuvo.bakar@sydney.edu.au |
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Guest lecturer(s) | Kylie-Ann Mallitt, kylie-ann.mallitt@sydney.edu.au |
Lecturer(s) | Shuvo Bakar, shuvo.bakar@sydney.edu.au |