Cancer Statistics and Epidemiology
| Code | School | Level | Credits | Semesters |
| ONCG4007 | Cancer and Stem Cells | 4 | 10 | Spring UK |
- Code
- ONCG4007
- School
- Cancer and Stem Cells
- Level
- 4
- Credits
- 10
- Semesters
- Spring UK
Summary
This module will give students a basic understanding of the principles underlying the design and analysis of epidemiological studies and clinical trials. Topics will include: study design, bias and confounding, sampling variation, summarising and presenting data, measures of effect, hypothesis testing (t-test, chi-squared test), survival and longitudinal data, meta-analysis, non-parametric methods, correlation, introduction to multivariate regression analysis, screening, sample size and power.
Target Students
Core module for MSc Oncology and MSc Cancer Immunology and Biotechnology students. Also PG Cert Oncology students.
Classes
One two-hour lecture per week (18 hours).
Assessment
- 30% Assignment 1: Analyse dataset using SPSS and write a report of 1500-2000 words (5 tables/graphs)
- 70% In Class Exam 1 (Written) (2-hour): Multiple choice and short answer questions (2 hours)
Assessed by end of spring semester
Educational Aims
To give students a basic understanding of cancer epidemiology and key epidemiological concepts, including prevalence and incidence, study design, and how causes can be established. To give a basic understanding of statistical principles underlying the design and analysis of clinical trials, studies of aetiology and prognosis, and the evaluation of screening tests.Learning Outcomes
By the end of this module, students will be able to:
• Explain basic epidemiological concepts
• Choose the most appropriate design for a research study and describe the main features, strengths and limitations of each design
• For the basic statistical methods used in epidemiology research, carry out the appropriate analysis in SPSS and interpret the resulting output
• Explain the basic concepts of other methodologies used in epidemiology and clinical research, including screening and meta-analysis