Description
The course will cover a range of advanced statistical techniques and methodological approaches used in records research. In particular:
- More advanced regression techniques, including logistic regression and Cox modelling,
- Methods for handling missing data with a focus on multiple imputation.
- Introduction to causal inference: This part will cover:
- Association vs causation
- Potential outcome framework
- Causal diagrams
- G-methods
- Assumptions that underlie all causal inferences
By the end of the module, you should be able to:
- critically assess and use a variety of statistical methods and approaches relevant to electronic health records research;
- select appropriate methods to draw causal inferences from data sets;
- demonstrate an understanding of the principles and assumptions of the various statistical approaches and be able to critically evaluate these;
- design an appropriate analysis plan to address a specific research question;
- demonstrate knowledge and understanding to support the choice of appropriate statistical methods to answer a range of questions.
You will learn though a combination of lectures and computer-based practicals using Stata and R.
Kirkwood & Sterne (2010) Essential Medical Statistics. Malden, Mass. : Blackwell Science
Kenneth J. Rothman, Sander Greenland, Timothy L. Lash (2008) Modern Epidemiology. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins
Kenward, M. G., & Carpenter, J. (2013). Multiple Imputation and its Application. Chichester: John Wiley & Sons
Kenward, M. G., & Carpenter, J. (2007). Multiple Imputation: current perspectives. Statistical Methods in Medical Research, 16(3), 199-218
Hernán MA, Robins JM (2019). Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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