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Introduction to and revision of conventional methods for contingency tables especially in epidemiology:
- Odds ratios and relative risks;
- Chi-squared tests for independence;
- Mantel Haenszel methods for stratified tables;
- Methods for paired data.
The exponential family of distributions;
- Generalized Linear Models (GLMs);
- Parameter estimation for GLMs;
- Inference for GLMs, including the use of score, Wald and deviance statistics (including residuals) for confidence intervals and hypothesis tests.
Binary variables and logistic regression models:
- Methods for assessing model adequacy;
- Nominal and ordinal logistic regression for categorical response variables with more than two categories;
Count data and Poisson regression models:
- Log-linear models.
- Fitting GLMs in Stata and R.
Intended learning outcomes
- Understand the mathematical theory behind generalised linear models (GLMs) to analyse categorical data with proper attention to the underlying assumptions.
- Appreciate that most conventional methods of analysis of contingency table data are special cases of GLMs.
- Operate the Stata and R statistical packages to fit GLMs to data, extract, summarise, present and report the results.
- Emphasise the importance of the practical interpretation and communication of results to colleagues and clients who are not statisticians.
- Independent problem solving,
- Facility with abstract reasoning,
- Clarity of written expression,
- Sound communication of technical concepts
Last updated: 6 December 2019