Further Medical Statistics

Code

EPID4017

School

School of Medicine

Level

4

Credits

10

Semesters

Spring UK

Summary

Summary of Content:   

This module will cover: 

●     The application of a wide range of statistical approaches to analysing public health and other health data   

●     Regression analyses and approaches for modelling more than one independent variable.  

Regression models for different types of outcomes including: multiple logistical regression and multiple linear regression. 



●     An introduction to the concept of likelihood and the likelihood ratio test 
 

●     Strategies of multivariable modelling including adjustment for cofounders and risk prediction models
 

●     Introduction to assumptions of missing data and methods including imputation methods for dealing with missing data 
 

●    Other complex analyses for dealing with other types of data including longitudinal or scale data  

This module equips students with the knowledge and skills to analyse and interpret public health and other health or epidemiological data, including advanced methods that allow us to investigate the effects of individual and multiple factors on different types of health outcome in different study designs. The module concentrates on the practical application of these statistical methods and approaches in analysing health data, providing the essential statistical theory to underpin these methods and to appreciate when it is appropriate to use different  
methods.

Target Students

Primarily postgraduate students on the Master of Public Health, Master of Public Health (Global Health) Master of Public Health (Health Research) and MSc Health Psychology Students. Places will be available to new staff and PhD students in School of Medicine who require training as part of their research role subject.

Classes

• One 2-hour lecture each week for 12 weeks

Assessment

• 100% Coursework: Portfolio equivalent to 2,500 words

Assessed by end of spring semester

Educational Aims

Learning Outcomes

Knowledge and understanding. Students will be able to demonstrate:

• comprehensive knowledge and understanding of more advanced statistical methods, including regression modelling, the assumptions that underlie these methods, and their application to analyse public health, health psychology or other health data 

Intellectual skills. Students will be able to demonstrate:

• the skills to identify appropriate statistical methods to test original hypotheses and to allow for potential confounders, to apply them in original contexts, and to appropriately interpret the results.
• the ability to critically appraise and interpret evidence from studies using advanced statistical methods in the public health and health psychology literature
   

Professional practical skills. Students will be able to

• analyse, interpret and present, health data using advanced statistical methods, and use analysis packages such as R, interpret, and prepare scientific reports based on, health data, with appropriate evidence-based conclusions.
   

Transferable (key) skills. Students will be able to
 

• use electronic information systems to communicate, retrieve and send information and analyse data
• systematically evaluate written and numeric information drawing justified conclusions from the evidence
   

Conveners

• Dr Sonia Gran
• Professor Tricia Mckeever