Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.
Principles of statistics 1

Module leads

Sarah Lewington Sarah Lewington

Maria Quigley Maria Quigley

Derrick Bennett

Sarah Parish

Learning objectives:

  • To understand the underlying principles of medical statistics
  • To gain the technical skills to conduct appropriate statistical analyses independently
  • To produce appropriate publication-quality presentations (figures and tables) of statistical analyses

Sessions:

  1. Introduction to biostatistics and STATA
  2. Data distributions and descriptive statistics
  3. Normal distribution: sampling variations and statistical inference (CIs)
  4. Hypothesis testing: comparison of continuous variables between groups
  5. Prevalence, risks, odds and rates
  6. Studying exposure effects using prevalence, risks, odds and rates
  7. Non-parametric methods
  8. Linear regression and correlation
  9. ANOVA
  10. Further analysis of categorical data
  11. Introduction to logistic regression
  12. Confounding and effect modification using stratification and logistic regression
  13. Further logistic regression and conditional logistic regression
  14. Use of causal diagrams in epidemiological analyses
  15. Power and sample size calculations
  16. Display and documentation of results
  17. Introduction to summative assessment
  18. Review of module
  19. Poisson regression
  20. Survival analysis I: Introduction, censoring, life tables, life expectancy
  21. Survival analysis II: Kaplan-Meier, log rank
  22. Survival analysis III: Introduction to Cox regression
  23. Interactions (multiplicative and additive models)
  24. Attributable fractions, including adjusted AFs
  25. Absolute risk prediction and risk scores including lifetime risk
  26. Appropriate analyses of epidemiological data I: Measurement error & regression dilution bias
  27. Missing data and multiple imputation
  28. Repeated measurements
  29. Appropriate analysis of epidemiological data II: Choosing cut-offs floating absolute risks 
  30. Review of module